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Haiping Ma

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19 papers

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AAAI Conference 2026 Conference Paper

Debiased Cognitive Diagnosis: A Contrastive Counterfactual Modeling Method via Variational Autoencoder

Shangshang Yang
Xuewen Duan
Xiaoshan Yu
Ziwen Wang
Haiping Ma
Xingyi Zhang

Cognitive diagnosis (CD), inferring student knowledge mastery based on historical response records, is crucial for personalized educational services such as adaptive practice and learning path planning. Existing CD models were built based on the assumption that student's response data is integral, overlooking the nonrandom missingness of data caused by student answering exercises selectively. This missingness generally leads to biased and incomplete observations, where confounders, such as selection bias and exposure bias, significantly undermine the accuracy of student knowledge modeling. To address missingness, we propose a Debiased Cognitive Diagnosis (DBCD) framework through the perspective of counterfactual modeling to remove exogenous confounders from the response data. Specifically, the proposed DBCD achieves debiasing for CD by applying the idea of contrastive learning to constrain the model's prediction distributions on both factual and counterfactual data. For a student, the factual data is his/her original response records, while the counterfactual data is generated by sampling the same number of exercises from all exercises of each concept through a similarity-based counterfactual sampling strategy. Considering the difficulty of directly removing the exogenous confounders for student, we devise a β-Variational Autoencoder to model their exogenous confounders within the latent representations of knowledge proficiency by leveraging exercise priors and student response patterns. Then, the learned representations are further combined with the vanilla student's ability embedding via a gating mechanism-based fusion for final diagnosis prediction of the model. Extensive experiments on real-world educational datasets demonstrate that the proposed DBCD effectively mitigates confounders and even outperforms existing methods, thereby validating the feasibility and effectiveness of the DBCD framework.

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AAAI Conference 2026 Conference Paper

PEOAT: Personalization-Guided Evolutionary Question Assembly for One-Shot Adaptive Testing

Xiaoshan Yu
Ziwei Huang
Shangshang Yang
Ziwen Wang
Haiping Ma
Xingyi Zhang

With the rapid advancement of intelligent education, Computerized Adaptive Testing (CAT) has attracted increasing attention by integrating educational psychology with deep learning technologies. Unlike traditional paper-and-pencil testing, CAT aims to efficiently and accurately assess ex- aminee abilities by adaptively selecting the most suitable items during the assessment process. However, its real-time and sequential nature presents limitations in practical scenarios, particularly in large-scale assessments where interaction costs are high, or in sensitive domains such as psychological evaluations where minimizing noise and interfer- ence is essential. These challenges constrain the applicability of conventional CAT methods in time-sensitive or resource- constrained environments. To this end, we first introduce a novel task called one-shot adaptive testing (OAT), which aims to select a fixed set of optimal items for each test-taker in a one-time selection. Meanwhile, we propose PEOAT, a Personalization-guided Evolutionary question assembly framework for One-hot Adaptive Testing from the perspec- tive of combinatorial optimization. Specifically, we began by designing a personalization-aware initialization strategy that integrates differences between examinee ability and ex- ercise difficulty, using multi-strategy sampling to construct a diverse and informative initial population. Building on this, we proposed a cognitive-enhanced evolutionary framework incorporating schema-preserving crossover and cognitively guided mutation to enable efficient exploration through infor- mative signals. To maintain diversity without compromising fitness, we further introduced a diversity-aware environmen- tal selection mechanism. The effectiveness of PEOAT is val- idated through extensive experiments on two datasets, com- plemented by case studies that uncovered valuable insights.

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TIST Journal 2026 Journal Article

Reconciling Cognitive Modeling with Knowledge Forgetting: A Continuous Time-aware General Neural Network Framework

Ziwen Wang
Jingyuan Wang
Haiping Ma
Hengshu Zhu
Shangshang Yang
Xiaoshan Yu
Shuhuan Liu
Haifeng Zhang

Cognitive modeling, as an emerging technology in the field of computer-aided education, aims to explore students’ knowledge levels and learning abilities to achieve various intelligent educational applications. Although some existing work focuses on addressing the problem of student forgetting, it is still a less explored area how to naturally integrate the forgetting effect caused by the time interval between answering exercises into student knowledge state modeling. Additionally, traditional cognitive modeling methods mostly assume that students answer exercises one by one, which often does not align with real answering behavior and cannot be directly extended to diverse learning scenarios. Therefore, in this article, we propose a Continuous Time-based Neural Cognitive (CT-NC) framework and several implemented models (CT-NCM and two extensions) to effectively integrate the dynamic and continuous characteristics of knowledge forgetting into student learning process modeling, making it more natural. Specifically, we adopt a specially designed learning event encoding method to adjust the neural Hawkes process to capture the relationship between knowledge learning and forgetting over continuous time. Furthermore, we propose a customizable learning function to jointly model the changes in different knowledge states and their interaction with each practice moment. In the end, we demonstrate an extension CT-NCM+ that can adapt well to diverse learning scenarios, indicating that CT-NCM can solve real-world problems by flexibly adjusting its structure. Extensive experimental results on real datasets clearly demonstrate that CT-NCM and CT-NCM+ outperform the current state-of-the-art KT methods in student performance prediction, while our work points out a realistic research direction for KT and demonstrates its interpretability in knowledge learning visualization.

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AAAI Conference 2025 Conference Paper

AD4CD: Causal-Guided Anomaly Detection for Enhancing Cognitive Diagnosis

Haiping Ma
Yue Yao
Changqian Wang
Siyu Song
Yong Yang

Cognitive diagnosis is a key task in computer-aided education, aimed at assessing a students' proficiency in specific knowledge concepts based on their responses to exercises. However, existing cognitive diagnosis models often overlook anomalies in students and exercises. For instance, some students might incorrectly response exercises despite having a strong grasp of the knowledge concept, or they might response correctly despite a lack of understanding. Such subtle anomalies can adversely affect the diagnostic results of the models. To address these anomalies, we conduct a qualitative analysis of how anomalous student states and exercise properties impact response outcomes using causal diagrams. We propose a framework named Anomaly Detection for Cognitive Diagnosis (AD4CD) to enhance the ability of Learning-to-Detect-Anomalous. AD4CD approaches the problem from a causal perspective, analyzing confounding paths that affect the true causal relationship between student ability and response outcomes, and designing an anomaly detection mechanism suitable for cognitive diagnostic models. Specifically, we first account for anomalous student behaviors and exercise properties and introduce response times from both students and exercises as modeling factors. By quantifying the response time distributions in high-dimensional features, we identify anomalies within skewed distributions, including both left-tail and right-tail anomalies. Using the detected anomaly scores, we comprehensively model the students' anomalous behaviors and exercise anomalies. Additionally, we reconstruct unbiased true abilities under natural conditions and use reconstruction loss as an anomaly score to assist in modeling guessing and slipping features. Lastly, AD4CD leverages a general cognitive diagnosis model as its backbone, optimizing the guessing and slipping features to provide unbiased and accurate feedback. Extensive experimental results demonstrate that AD4CD effectively captures anomalous data in the diagnostic process across three real-world datasets, enhancing the accuracy of the diagnostic results.

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IJCAI Conference 2025 Conference Paper

Endowing Interpretability for Neural Cognitive Diagnosis by Efficient Kolmogorov-Arnold Networks

Shangshang Yang
Linrui Qin
Xiaoshan Yu
Ziwen Wang
Xueming Yan
Haiping Ma
Ye Tian

Cognitive diagnosis is crucial for intelligent education because of its ability to reveal students' proficiency in knowledge concepts. Although neural network-based neural cognitive diagnosis models (CDMs) have exhibited significantly better performance than traditional models, neural cognitive diagnosis is criticized for the poor model interpretability due to the multi-layer perceptron(MLP) employed, even with the monotonicity assumption. Therefore, this paper proposes to empower the interpretability of neural cognitive diagnosis models through efficient Kolmogorov-Arnold networks (KANs), named KAN2CD, where KANs are used to enhance interpretability in two manners. Specifically, in the first manner, KANs are directly used to replace the used MLPs in existing neural CDMs; while in the second manner, the student embedding, exercise embedding, and concept embedding are directly processed by several KANs, and then their outputs are further combined and learned in a unified KAN to get final predictions. Besides, the implementation of original KANs is modified without affecting the interpretability to overcome the problem of training KANs slowly. Extensive experiments show KAN2CD outperforms traditional CDMs and slightly surpasses existing neural CDMs, and its learned structures ensure interpretability on par with traditional CDMs and better than neural CDMs. The datasets, associated code, and more experimental results are available at https: //github. com/null233QAQ/KAN2CD.

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AAAI Conference 2025 Conference Paper

Explicit and Implicit Examinee-Question Relation Exploiting for Efficient Computerized Adaptive Testing

Changqian Wang
Shangshang Yang
Siyu Song
Ziwen Wang
Haiping Ma
Xingyi Zhang
Bo Jin

Computerized adaptive testing(CAT) is a crucial task in computer-aided education, which aims to adaptively select suitable question to diagnose examinees' ability status. Existing CAT approaches enhance selection performance by exploring examinee-question(E-Q) relation. These approaches either exclusively utilize explicit E-Q relation. For instance, policy-based approaches determine question selection based on predefined criteria. While effective in adapting to changes in question banks, these methods often entail significant computational costs in searching for suitable questions. Conversely, some studies focus solely on implicit E-Q relation. For example, learning-based approaches train agents to efficiently select questions by learning from large-scale datasets. However, they may struggle with newly introduced questions. Additionally, most of these existing question selectors are based on greedy strategies, which potentially overlooks promising quuestions. To bridge the above two types of approaches, we propose a novel framework named Relation Exploiting-based CAT(RECAT) by exploring and exploiting the implicit and explicit examinee-question relation. Specifically, we first define an examinee true ability-oriented selection objective to select more suitable questions. Then, to learn the implicit E-Q relation, we design a question selector, which explores the examinee ability and generates best-fitting questions for specific examinee ability from two aspects, including generation consistency and knowledge matching. The former aims to maximize the likelihood estimation of the implicit E-Q relation learning process, while the latter is employed to fit the distribution of real questions. To fully exploit explicit E-Q relation, we generate a high-quality candidate set for the given examinee's ability using implicit E-Q relation, which streamlines the search process, minimizing selection latency. We demonstrate the effectiveness and efficiency of our framework through comprehensive experiments on real-world datasets.

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IJCAI Conference 2024 Conference Paper

DGCD: An Adaptive Denoising GNN for Group-level Cognitive Diagnosis

Haiping Ma
Siyu Song
Chuan Qin
Xiaoshan Yu
Limiao Zhang
Xingyi Zhang
Hengshu Zhu

Group-level cognitive diagnosis, pivotal in intelligent education, aims to effectively assess group-level knowledge proficiency by modeling the learning behaviors of individuals within the group. Existing methods typically conceptualize the group as an abstract entity or aggregate the knowledge levels of all members to represent the group’s overall ability. However, these methods neglect the high-order connectivity among groups, students, and exercises within the context of group learning activities, along with the noise present in their interactions, resulting in less robust and suboptimal diagnosis performance. To this end, in this paper, we propose DGCD, an adaptive Denoising graph neural network for realizing effective Group-level Cognitive Diagnosis. Specifically, we first construct a group-student-exercise (GSE) graph to explicitly model higher-order connectivity among groups, students, and exercises, contributing to the acquisition of informative representations. Then, we carefully design an adaptive denoising module, integrated into the graph neural network, to model the reliability distribution of student-exercise edges for mining purer interaction features. In particular, edges of lower reliability are more prone to exclusion, thereby reducing the impact of noisy interactions. Furthermore, recognizing the relational imbalance in the GSE graph, which could potentially introduce bias during message passing, we propose an entropy-weighted balance module to mitigate such bias. Finally, extensive experiments conducted on four real-world educational datasets clearly demonstrate the effectiveness of our proposed DGCD model. The code is available at https: //github. com/BIMK/Intelligent-Education/tree/main/DGCD.

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NeurIPS Conference 2024 Conference Paper

DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis

Shangshang Yang
Mingyang Chen
Ziwen Wang
Xiaoshan Yu
Panpan Zhang
Haiping Ma
Xingyi Zhang

Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poorly, failing to provide high robustness against noise in students' interactions. Besides, lower-order exercise latent representations obtained in shallow layers are not well explored when learning the student representation. To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency graphs, respectively. Specifically, the latter two graphs are first disentangled from the interaction graph. Then, the student representation is learned from the interaction graph by a devised meta multigraph learning module; multiple learnable propagation paths in this module enable current student latent representation to access lower-order exercise latent representations, which can lead to more effective nad robust student representations learned; the exercise and concept representations are learned on the relation and dependency graphs by graph attention modules. Finally, a novel diagnostic function is devised to handle three disentangled representations for prediction. Experiments show better performance and robustness of DisenGCD than state-of-the-art CD methods and demonstrate the effectiveness of the disentangled learning framework and meta multigraph module. The source code is available at https: //github. com/BIMK/Intelligent-Education/tree/main/DisenGCD.

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AAAI Conference 2024 Conference Paper

Enhancing Cognitive Diagnosis Using Un-interacted Exercises: A Collaboration-Aware Mixed Sampling Approach

Haiping Ma
Changqian Wang
Hengshu Zhu
Shangshang Yang
Xiaoming Zhang
Xingyi Zhang

Cognitive diagnosis is a crucial task in computer-aided education, aimed at evaluating students' proficiency levels across various knowledge concepts through exercises. Current models, however, primarily rely on students' answered exercises, neglecting the complex and rich information contained in un-interacted exercises. While recent research has attempted to leverage the data within un-interacted exercises linked to interacted knowledge concepts, aiming to address the long-tail issue, these studies fail to fully explore the informative, un-interacted exercises related to broader knowledge concepts. This oversight results in diminished performance when these models are applied to comprehensive datasets. In response to this gap, we present the Collaborative-aware Mixed Exercise Sampling (CMES) framework, which can effectively exploit the information present in un-interacted exercises linked to un-interacted knowledge concepts. Specifically, we introduce a novel universal sampling module where the training samples comprise not merely raw data slices, but enhanced samples generated by combining weight-enhanced attention mixture techniques. Given the necessity of real response labels in cognitive diagnosis, we also propose a ranking-based pseudo feedback module to regulate students' responses on generated exercises. The versatility of the CMES framework bolsters existing models and improves their adaptability. Finally, we demonstrate the effectiveness and interpretability of our framework through comprehensive experiments on real-world datasets.

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TIST Journal 2024 Journal Article

Model-Agnostic Adaptive Testing for Intelligent Education Systems via Meta-learned Gradient Embeddings

Haoyang Bi
Qi Liu
Han Wu
Weidong He
Zhenya Huang
Yu Yin
Haiping Ma
Yu Su

The field of education has undergone a significant revolution with the advent of intelligent systems and technology, which aim to personalize the learning experience, catering to the unique needs and abilities of individual learners. In this pursuit, a fundamental challenge is designing proper test for assessing the students’ cognitive status on knowledge and skills accurately and efficiently. One promising approach, referred to as Computerized Adaptive Testing (CAT), is to administrate computer-automated tests that alternately select the next item for each examinee and estimate their cognitive states given their responses to the selected items. Nevertheless, existing CAT systems suffer from inflexibility in item selection and ineffectiveness in cognitive state estimation, respectively. In this article, we propose a Model-Agnostic adaptive testing framework via Meta-leaned Gradient Embeddings, MAMGE for short, improving both item selection and cognitive state estimation simultaneously. For item selection, we design a Gradient Embedding-based Item Selector (GEIS) which incorporates the concept of gradient embeddings to represent items and selects the best ones that are both informative and representative. For cognitive state estimation, we propose a Meta-learned Cognitive State Estimator (MCSE) to automatically control the estimation process by learning to learn a proper initialization and dynamically inferred updates. Both MCSE and GEIS are inherently model-agnostic, and the two modules have an ingenious connection via meta-learned gradient embeddings. Finally, extensive experiments evaluate the effectiveness and flexibility of MAMGE.

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EAAI Journal 2024 Journal Article

Weighted multi-error information entropy based you only look once network for underwater object detection

Haiping Ma
Yajing Zhang
Shengyi Sun
Weijia Zhang
Minrui Fei
Huiyu Zhou

Underwater object detection is considered as one of the most challenging issues in computer vision. In this paper, a weighted multi-error information entropy based YOLO (You Only Look Once) network is proposed to address underwater illumination noise affecting the detection accuracy. First, underwater illumination is essentially structural and non-uniform, and it is modeled as an independent and piecewise identical distribution, which is a generic noise model to describe the complex underwater illuminating environment and accommodates the traditional Gaussian distribution as a special case. Second, assisted by the proposed illumination noise model, a minimum weighted error entropy criterion, which is an information-theoretic learning method, is introduced into the loss function of YOLO network, and then the network parameters are trained and optimized to improve the detection performance. Furthermore, a multi-error processing strategy is simultaneously used to handle vector errors during information back-propagation in order to accelerate convergence. Experiments on underwater object detection datasets including URPC2018, URPC2019 and Enhanced dataset, show the proposed weighted multi-error information entropy based YOLOv8 network gets mean average precision (MAP) of 88. 7%, 91. 8% and 96. 7% respectively, and average frames per second (FPS) of 116. 6. These two evaluation metrics are better than the baseline YOLOv8 and the existing advanced non-YOLO approaches by at least 5. 2% and 5. 3% respectively. The results verify the effectiveness and superiority of the proposed network for underwater object detection in complex underwater environment.

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NeurIPS Conference 2023 Conference Paper

Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing

Shangshang Yang
Xiaoshan Yu
Ye Tian
Xueming Yan
Haiping Ma
Xingyi Zhang

Knowledge tracing (KT) aims to trace students' knowledge states by predicting whether students answer correctly on exercises. Despite the excellent performance of existing Transformer-based KT approaches, they are criticized for the manually selected input features for fusion and the defect of single global context modelling to directly capture students' forgetting behavior in KT, when the related records are distant from the current record in terms of time. To address the issues, this paper first considers adding convolution operations to the Transformer to enhance its local context modelling ability used for students' forgetting behavior, then proposes an evolutionary neural architecture search approach to automate the input feature selection and automatically determine where to apply which operation for achieving the balancing of the local/global context modelling. In the search space, the original global path containing the attention module in Transformer is replaced with the sum of a global path and a local path that could contain different convolutions, and the selection of input features is also considered. To search the best architecture, we employ an effective evolutionary algorithm to explore the search space and also suggest a search space reduction strategy to accelerate the convergence of the algorithm. Experimental results on the two largest and most challenging education datasets demonstrate the effectiveness of the architecture found by the proposed approach.

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AAAI Conference 2022 Conference Paper

Fully Adaptive Framework: Neural Computerized Adaptive Testing for Online Education

Yan Zhuang
Qi Liu
Zhenya Huang
Zhi Li
Shuanghong Shen
Haiping Ma

Computerized Adaptive Testing (CAT) refers to an efficient and personalized test mode in online education, aiming to accurately measure student proficiency level on the required subject/domain. The key component of CAT is the “adaptive” question selection algorithm, which automatically selects the best suited question for student based on his/her current estimated proficiency, reducing test length. Existing algorithms rely on some manually designed and pre-fixed informativeness/uncertainty metrics of question for selections, which is labor-intensive and not sufficient for capturing complex relations between students and questions. In this paper, we propose a fully adaptive framework named Neural Computerized Adaptive Testing (NCAT), which formally redefines CAT as a reinforcement learning problem and directly learns selection algorithm from real-world data. Specifically, a bilevel optimization is defined and simplified under CAT’s application scenarios to make the algorithm learnable. Furthermore, to address the CAT task effectively, we tackle it as an equivalent reinforcement learning problem and propose an attentive neural policy to model complex non-linear interactions. Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of NCAT compared with several state-of-the-art methods.

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IJCAI Conference 2022 Conference Paper

Reconciling Cognitive Modeling with Knowledge Forgetting: A Continuous Time-aware Neural Network Approach

Haiping Ma
Jingyuan Wang
Hengshu Zhu
Xin Xia
Haifeng Zhang
Xingyi Zhang
Lei Zhang

As an emerging technology of computer-aided education, cognitive modeling aims at discovering the knowledge proficiency or learning ability of students, which can enable a wide range of intelligent educational applications. While considerable efforts have been made in this direction, a long-standing research challenge is how to naturally integrate the forgetting mechanism into the learning process of knowledge concepts. To this end, in this paper, we propose a novel Continuous Time based Neural Cognitive Modeling(CT-NCM) approach to integrate the dynamism and continuity of knowledge forgetting into students' learning process modeling in a realistic manner. To be specific, we first adapt the neural Hawkes process with a specially-designed learning event encoding method to model the relationship between knowledge learning and forgetting with continuous time. Then, we propose a learning function with extendable settings to jointly model the change of different knowledge states and their interactions with the exercises at each moment. In this way, CT-NCM can simultaneously predict the future knowledge state and exercise performance of students. Finally, we conduct extensive experiments on five real-world datasets with various benchmark methods. The experimental results clearly validate the effectiveness of CT-NCM and show its interpretability in terms of knowledge learning visualization.

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EAAI Journal 2015 Journal Article

Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling

Haiping Ma
Shufei Su
Dan Simon
Minrui Fei

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling.

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EAAI Journal 2014 Journal Article

Hybrid biogeography-based evolutionary algorithms

Haiping Ma
Dan Simon
Minrui Fei
Xinzhan Shu
Zixiang Chen

Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EAs. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the continuous optimization benchmarks from the 2013 Congress on Evolutionary Computation (CEC) and on some real-world traveling salesman problems. The new hybrid EAs include two approaches to hybridization: (1) iteration-level hybridization, in which various EAs and BBO are executed in sequence; and (2) algorithm-level hybridization, which runs various EAs independently and then exchanges information between them using ideas from biogeography. Our empirical study shows that the new hybrid EAs significantly outperforms their constituent algorithms with the selected tuning parameters and generation limits, and algorithm-level hybridization is generally better than iteration-level hybridization. Results also show that the best new hybrid algorithm in this paper is competitive with the algorithms from the 2013 CEC competition. In addition, we show that the new hybrid EAs are generally robust to tuning parameters. In summary, the contribution of this paper is the introduction of biogeography-based hybridization strategies to the EA community.

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EAAI Journal 2013 Journal Article

On the equivalences and differences of evolutionary algorithms

Haiping Ma
Dan Simon
Minrui Fei
Zixiang Chen

Evolutionary algorithms (EAs) are fast and robust computation methods for global optimization, and have been widely used in many real-world applications. We first conceptually discuss the equivalences of various popular EAs including genetic algorithm (GA), biogeography-based optimization (BBO), differential evolution (DE), evolution strategy (ES) and particle swarm optimization (PSO). We find that the basic versions of BBO, DE, ES and PSO are equal to the GA with global uniform recombination (GA/GUR) under certain conditions. Then we discuss their differences based on biological motivations and implementation details, and point out that their distinctions enhance the diversity of EA research and applications. To further study the characteristics of various EAs, we compare the basic versions and advanced versions of GA, BBO, DE, ES and PSO to explore their optimization ability on a set of real-world continuous optimization problems. Empirical results show that among the basic versions of the algorithms, BBO performs best on the benchmarks that we studied. Among the advanced versions of the algorithms, DE and ES perform best on the benchmarks that we studied. However, our main conclusion is that the conceptual equivalence of the algorithms is supported by the fact that algorithmic modifications result in very different performance levels.

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EAAI Journal 2011 Journal Article

Analysis of migration models of biogeography-based optimization using Markov theory

Haiping Ma
Dan Simon

Biogeography-based optimization (BBO) is a new evolutionary algorithm inspired by biogeography, which involves the study of the migration of biological species between habitats. Previous work has shown that various migration models of BBO result in significant changes in performance. Sinusoidal migration models have been shown to provide the best performance so far. Motivated by biogeography theory and previous results, in this paper a generalized sinusoidal migration model curve is proposed. A previously derived BBO Markov model is used to analyze the effect of migration models on optimization performance, and new theoretical results which are confirmed with simulation results are obtained. The results show that the generalized sinusoidal migration model is significantly better than other models for simple but representative problems, including a unimodal one-max problem, a multimodal problem, and a deceptive problem. In addition, performance comparison is further investigated through 23 benchmark functions with a wide range of dimensions and diverse complexities, to verify the superiority of the generalized sinusoidal migration model.

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EAAI Journal 2011 Journal Article

Blended biogeography-based optimization for constrained optimization

Haiping Ma
Dan Simon

Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm optimization 2007 (SPSO 07). The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained single-objective optimization problems.

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