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Knowledge Migration Strategies for Optimization in Multi-Population Cultural Algorithms

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  • Wed, 04/26/2017 - 11:00am - 1:30pm

Knowledge Migration Strategies for Optimization of Multi-Population Cultural Algorithm

MSc Thesis Defense by:

Panth Parikh

Date:  Wednesday, April 26, 2017
Time:  11:00 am – 1:30 pm
Location: 3105, Lambton Tower

Abstract: Evolutionary Algorithms (EAs) have been used by many researchers to solve complex optimization problems. EAs are inspired by the biological model of evolution and process of natural selection. Cultural Algorithms (CAs) are a type of EAs that use knowledge for the evolution process. A CA which operates on multiple population spaces to provide heterogeneous search space is called Multi-Population Cultural Algorithm (MPCA). Migration of individuals in MPCA enhances diversity among the population and provides better search in the spaces for improved quality of solutions. This thesis describes the implementation of migration strategies in MPCA. The migration strategies are inspired from Game Theory. Migration strategies are related to the field of economics as it brings the social factor which allows the agents in population to use their knowledge to make decisions. The procedure provides strategic migration for better solutions in the search space. Game theory concepts like Prisoner’s Dilemma, Oligopoly, Duopoly, Fair Division and Intra-household Bargaining are used as migration strategies. The proposed model provides the impact of the migrating agent on both the migrating and destination populations. The proposed algorithm is evaluated against the CEC- 2015 expensive benchmark optimization problems which are a set of 15 different kinds of minimization problems. The proposed model is also evaluated and compared with other existing optimization algorithms. Results depict that incorporating strategies in MPCA provides a better quality of solutions on complex and high dimensional problems.

Thesis Committee:
Internal Reader:  Dr. Mehdi Kargar  
External Reader:  Dr. Mohammed Khalid
Advisor:  Dr. Ziad Kobti
Chair:  Dr. Dan Wu

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