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.
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Michalak, K. (2014). Sim-EA: An Evolutionary Algorithm Based on Problem Similarity. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_24
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DOI: https://doi.org/10.1007/978-3-319-10840-7_24
Publisher Name: Springer, Cham
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