Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
Genetic Algorithms in Engineering and Computer Science
Genetic Algorithms in Engineering and Computer Science
Myoelectric activity detection during a Sit-to-Stand movement using threshold methods
Computers & Mathematics with Applications
Hybrid harmony search and artificial bee colony algorithm for global optimization problems
Computers & Mathematics with Applications
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We present a new evolutionary algorithm-''learning algorithm'' for multimodal optimization. The scheme for reproducing a new generation is very simple. Control parameters, of the length of the list of historical best solutions and the ''learning probability'' of the current solutions being moved towards the current best solutions and towards the historical ones, are used to assign different search intensities to different parts of the feasible area and to direct the updating of the current solutions. Results of numerical tests on minimization of the 2D Schaffer function, the 2D Shubert function and the 10D Ackley function show that this algorithm is effective and efficient in finding multiple global solutions of multimodal optimization problems.