Abstract
In this paper a new evolutionary algorithm Sim-EA is presented. This algorithm is designed to tackle several instances of an optimization problem at once based on an assumption that it might be beneficial to share information between solutions of similar instances. The Sim-EA algorithm utilizes the concept of multipopulation optimization. Each subpopulation is assigned to solve one of the instances which are similar to each other. Problem instance similarity is expressed numerically and the value representing similarity of any pair of instances is used for controlling specimen migration between subpopulations tackling these two particular instances. © 2014 Springer International Publishing Switzerland.
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CITATION STYLE
Michalak, K. (2014). Sim-EA: An evolutionary algorithm based on problem similarity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8669 LNCS, pp. 191–198). Springer Verlag. https://doi.org/10.1007/978-3-319-10840-7_24
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