Probability, statistics, and queueing theory with computer science applications
Probability, statistics, and queueing theory with computer science applications
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning and evolution in neural networks
Adaptive Behavior
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Artificial Life
Sexual Selection for Genetic Algorithms
Artificial Intelligence Review
Lamarckian Evolution, The Baldwin Effect and Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Simulating Gender Separation With Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Adding learning to the cellular development of neural networks: Evolution and the baldwin effect
Evolutionary Computation
Adaptive genetic operators based on coevolution with fuzzybehaviors
IEEE Transactions on Evolutionary Computation
An Evolutionary Algorithm for Global Optimization Based on Level-Set Evolution and Latin Squares
IEEE Transactions on Evolutionary Computation
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This work presents a novel sexual adaptive genetic algorithm (NSAGA) based on two-step evolutionary scenario of Baldwin effect to overcome the shortcomings of traditional genetic algorithms, such as premature convergence, stochastic roaming, and poor capabilities in local exploring. NSAGA simulates sexual reproduction in nature and utilizes an effective gender determination method to divide the evolutionary population into two different gender subgroups. Based on the competition, cooperation, and innate differences between two gender subgroups, NSAGA adaptively adjusts the sexual genetic operators. To guide the individuals' evolution, NSAGA adopts a two-step evolutionary scenario: NSAGA guides individuals in niche to forward or reverse evolutionary learning inspired by the acquired reinforcement learning theory based on Baldwin effect, and enables the transmission of fitness information between parents and offspring to supervise the offspring's evolution. Then, the global convergence analysis of NSAGA is presented in detail. It is theoretically proved that NSAGA can converge to the global optimum and the epsilon-optimal solution with probability one. Moreover, numerical simulations are conducted for a set of benchmark test functions, and the performance of NSAGA is compared with that of some evolutionary algorithms published recently. Experiments results show that the proposed algorithm is effective and advantageous.