Author Archives: Community

Adaptive Multimodal Continuous Ant Colony Optimization

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization (ACO) algorithms in preserving high diversity, this paper intends to … Continue reading

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Factored Evolutionary Algorithms

Factored evolutionary algorithms (FEAs) are a new class of evolutionary search-based optimization algorithms that have successfully been applied to various problems, such as training neural networks and performing abductive inference in graphical model… Continue reading

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Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes

Recently, a number of high performance many-objective evolutionary algorithms with systematically generated weight vectors have been proposed in the literature. Those algorithms often show surprisingly good performance on widely used DTLZ and WFG test problems. The performance of those algorithms has continued to be improved. The aim of this paper is to show our concern that such a performance improvement race may lead to the overspecialization of developed algorithms for the frequently used many-objective test problems. In this paper, we first explain the DTLZ and WFG test problems. Next, we explain many-objective evolutionary algorithms characterized by the use of systematically generated weight vectors. Then we discuss the relation between the features of the test problems and the search mechanisms of weight vector-based algorithms such as multiobjective evolutionary algorithm based on decomposition (MOEA/D), nondominated sorting genetic algorithm III (NSGA-III), MOEA/dominance and decomposition (MOEA/DD), and θ-dominance based evolutionary algorithm (θ-DEA). Through computational experiments, we demonstrate that a slight change in the problem formulations of DTLZ and WFG deteriorates the performance of those algorithms. After explaining the reason for the performance deterioration, we discuss the necessity of more general test problems and more flexible algorithms. Continue reading

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Heterogeneous Cooperative Co-Evolution Memetic Differential Evolution Algorithm for Big Data Optimization Problems

Evolutionary algorithms (EAs) have recently been suggested as a candidate for solving big data optimization problems that involve a very large number of variables and need to be analyzed in a short period of time. However, EAs face a scalability issue … Continue reading

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IEEE Transactions on Evolutionary Computation publication information

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Table of contents

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IEEE Transactions on Evolutionary Computation information for authors

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. Continue reading

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Quantifying Variable Interactions in Continuous Optimization Problems

Interactions between decision variables typically make an optimization problem challenging for an evolutionary algorithm (EA) to solve. Exploratory landscape analysis (ELA) techniques can be used to quantify the level of variable interactions in an opt… Continue reading

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IEEE World Congress on Computational Intelligence

Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers. Continue reading

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A Multiobjective Cooperative Coevolutionary Algorithm for Hyperspectral Sparse Unmixing

Sparse unmixing of hyperspectral data is an important technique aiming at estimating the fractional abundances of the endmembers. Traditional sparse unmixing is faced with the $boldsymbol {l_{0}}$ -norm problem which is an NP-hard problem. Sparse unmi… Continue reading

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