Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Genetic drift in genetic algorithm selection schemes
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Elitism-based compact genetic algorithms
IEEE Transactions on Evolutionary Computation
Orthogonal exploration of the search space in evolutionary test case generation
Proceedings of the 2013 International Symposium on Software Testing and Analysis
Information and Software Technology
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Meta-heuristics have been successfully used to solve a wide variety of problems. However, one issue many techniques have is their risk of being trapped into local optima, or to create a limited variety of solutions (problem known as "population drift"). During recent and past years, different kinds of techniques have been proposed to deal with population drift, for example hybridizing genetic algorithms with local search techniques or using niche techniques. This paper proposes a technique, based on Singular Value Decomposition (SVD), to enhance Genetic Algorithms (GAs) population diversity. SVD helps to estimate the evolution direction and drive next generations towards orthogonal dimensions. The proposed SVD-based GA has been evaluated on 11 benchmark problems and compared with a simple GA and a GA with a distance-crowding schema. Results indicate that SVD-based GA achieves significantly better solutions and exhibits a quicker convergence than the alternative techniques.