Pair Bonds In Genetic Algorithm

• Main Author: Lim , Ting Yee
• Format: Thesis
• Language: English
• Published: 2015
• Subjects: QA75.5-76.95 Electronic computers. Computer science
• Online Access: http://eprints.usm.my/31889/1/LIM_TING_YEE_%28HJ%29.pdf; http://eprints.usm.my/31889/
• Institution: Universiti Sains Malaysia

Tesis ini membentangkan siasatan komprehensif berasaskan konsep ikatan pasangan (pasangan monogami) yang akan dilaksanakan dalam fasa rekombinasi algoritma genetik (GA). GA merupakan teknik pencarian heuristik berdasarkan prinsip dan mekanisma pilihan semula jadi dan teori "survival of the fittest". Biasanya kromosom ibubapa akan dipilih pada setiap generasi bagi menghasilkan kromosom anak melalui operasi percantuman (crossover) dan mutasi. Proses ini diulangi sehingga syarat berhenti dipenuhi. Tetapi kadang-kala alam semula jadi mempamerkan pembentukan hubungan yang berkekalan antara pasangan mengawan. Dalam masyarakat manusia moden, sesetengah burung, ikan, tikus, dan cicak, ikatan pasangan merupakan aspek penting dalam tingkah laku sosial mereka. Mereka biasanya mengekalkan pasangan yang sama sepanjang hidup - monogami sosial. Oleh itu, tesis ini mengkaji kesesuaian aplikasi ikatan pasangan dalam GA. Dua kaedah GA baru akan dibentangkan: Kaedah pertama dikenali sebagai Algoritma Genetik Ikatan Monogami (MopGA). Dalam MopGA, kromosom ibubapa akan berkekalan sehingga beberapa generasi. 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.