Pair Bonds In Genetic Algorithm

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Date
2015-07
Authors
Lim, Ting Yee
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This work presents a comprehensive investigation on the concept of pair bonds (monogamous pairs) for the mating phase of genetic algorithms (GAs). GA is a heuristic search technique based on the principles and mechanisms of natural selection. Traditionally, parents are selected at every generation to reproduce offspring through crossover and mutation operations. The process reiterates until some termination conditions are met. However, nature sometimes exhibits the formation of enduring relationships between mating partners. In modern human society, some avian models, fish, rodents, and even lizards, pair bonds are integral aspects of their social behaviour. These species usually share the same mating partners throughout their lifetime - socially monogamous. Taking the cue from nature, this thesis studies the feasibilities of pair bonds in GA. Consequently, two methodologies are proposed: Firstly, in the Monogamous Pairs Genetic Algorithm (MopGA), parents are bonded and mated consistently over several predefined generations. Selection of new parents pairs will only take place at the end of pair bond tenure. Meanwhile, competition occurs between siblings to ensure only the best offspring are retained. Occasional infidelity generates variety, spreads genetic information across the population and speeds up convergence. Secondly, to improve the ease-of-use of MopGA, an adaptive MopGA (AMopGA) is introduced. Algorithm sensitive-parameters are tuned adaptively throughout the evolutionary process - further improving the performance. Rigorous performance investigation of both methodologies are carried out on different notable benchmark problems. The results reveal that the algorithms are very competitive to existing approaches in terms of solution quality as well as computational effort.
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Electronic Computer
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