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Chapter and Conference Paper
Halftone Image Generation with Improved Multiobjective Genetic Algorithm
A halftoning technique that uses a simple GA has proven to be very effective to generate high quality halftone images. Recently, the two major drawbacks of this conventional halftoning technique with GAs, i.e....
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Chapter and Conference Paper
Parallel Varying Mutation in Deterministic and Self-adaptive GAs
In this work we study varying mutations applied either serial or parallel to crossover and discuss its effect on the performance of deterministic and self-adaptive varying mutation GAs. After comparative exper...
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Chapter and Conference Paper
Genetic Algorithms on NK-Landscapes: Effects of Selection, Drift, Mutation, and Recombination
Empirical studies have shown that the overall performance of random bit climbers on NK-Landscapes is superior to the performance of some simple and enhanced GAs. Analytical studies have also lead to suggest th...
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Chapter and Conference Paper
Selection, Drift, Recombination, and Mutation in Multiobjective Evolutionary Algorithms on Scalable MNK-Landscapes
This work focuses on the working principles, behavior, and performance of state of the art multiobjective evolutionary algorithms (MOEAs) on discrete search spaces by using MNK-Landscapes. Its motivation comes...
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Chapter and Conference Paper
Controlling Dominance Area of Solutions and Its Impact on the Performance of MOEAs
This work proposes a method to control the dominance area of solutions in order to induce appropriate ranking of solutions for the problem at hand, enhance selection, and improve the performance of MOEAs on co...
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Chapter and Conference Paper
Self-Controlling Dominance Area of Solutions in Evolutionary Many-Objective Optimization
Controlling dominance area of solutions (CDAS) relaxes the concepts of Pareto dominance with an user-defined parameter S. This method enhances the search performance of dominance-based MOEA in many-objective opti...
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Chapter and Conference Paper
Genetic Diversity and Effective Crossover in Evolutionary Many-objective Optimization
In this work, we analyze genetic diversity of Pareto optimal solutions (POS) and study effective crossover operators in evolutionary many-objective optimization. First we examine the diversity of genes in the ...
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Chapter and Conference Paper
Adaptive Control of the Number of Crossed Genes in Many-Objective Evolutionary Optimization
To realize effective genetic operation in evolutionary many-objective optimization, crossover controlling the number of crossed genes (CCG) has been proposed. CCG controls the number of crossed genes by using ...
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Chapter and Conference Paper
A Study on Large Population MOEA Using Adaptive ε-Box Dominance and Neighborhood Recombination for Many-Objective Optimization
Multi-objective evolutionary algorithms are increasingly being investigated to solve many-objective optimization problems. However, most algorithms recently proposed for many-objective optimization cannot find...
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Chapter and Conference Paper
Geometric Differential Evolution in MOEA/D: A Preliminary Study
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is an aggregation-based algorithm which has became successful for solving multi-objective optimization problems (MOPs). So far, for th...
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Chapter and Conference Paper
Feature Selection in Gait Classification Using Geometric PSO Assisted by SVM
Gait recognition is used to identify individuals by the way they walk. Recent research in automated human gait recognition has mainly focused on develo** robust features representations and matching algorith...
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Chapter and Conference Paper
Stacked Progressive Auto-Encoders for Clothing-Invariant Gait Recognition
Gait recognition has been considered as an unique and useful biometric for person identification at distance. However, variations in covariate factors such as view angles, clothing, and carrying condition can ...