Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Maintaining the Diversity of Genetic Programs
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
A Hybrid Evolutionary Algorithm for Multi-FPGA Systems Design
DSD '02 Proceedings of the Euromicro Symposium on Digital Systems Design
Compact Genetic Algorithm for Performance Improvement in Hierarchical Sensor Networks Management
ISPAN '05 Proceedings of the 8th International Symposium on Parallel Architectures,Algorithms and Networks
Particle swarm optimization with adaptive population size and its application
Applied Soft Computing
IEEE Transactions on Evolutionary Computation
A hybrid heuristic for the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Elitism-based compact genetic algorithms
IEEE Transactions on Evolutionary Computation
A family of compact genetic algorithms for intrinsic evolvable hardware
IEEE Transactions on Evolutionary Computation
Real-Valued Compact Genetic Algorithms for Embedded Microcontroller Optimization
IEEE Transactions on Evolutionary Computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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Abstract: Instead of the genetic operators such as crossover and mutation, compact Genetic Algorithms (cGAs) use a probability vector (PV) for the current population to reproduce offsprings of the next generation. Therefore, the original cGA can be easily implemented with no parameter tuning of the genetic operators and with reducing memory requirements. Many researchers have suggested their own schemes to improve the performance of the cGA, such as quality of solutions and convergence speed. However, these researches mainly have given fast convergence to the original cGA. They still have the premature convergence problem resulting in the low quality of solutions. Besides, the additional control parameters such as @h of ne-cGA are even required for several cGAs. We propose two new schemes, called cGABV (an acronym for cGA using belief vectors) and cGABVE (an acronym for cGABV with elitism), in order to improve the performance of conventional cGAs by maintaining the diversity of individuals. For this purpose, the proposed algorithms use a belief vector (BV) instead of a PV. Each element of the BV has a probability distribution with a mean and a variance, whereas each element of a PV has a singular probability value. Accordingly, the proposed BV enables to affect the performances by controlling the genetic diversity of each generation. In addition, we propose two variants of the proposed cGABV and cGABVE, Var1 and Var2, employing the entropy-driven parameter control scheme in order to avoid the difficulty of designing the control parameter (@l). Experimental results show that the proposed variants of cGAs outperform the conventional cGAs. For investigating the diversity of each cGA, the entropy is employed and calculated at each generation. Finally, we discuss the effect of @l related to the variances of the BV through the additional experiment.