Real-parameter optimization in dynamic environments using estimation of distribution algorithms

Author(s)

André Ricardo Gonçalves

• Document type: Master's Dissertation
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação
• Defense date: 2011-04-19

Examining board members

Fernando José Von Zuben; Leandro Nunes de Castro Silva; Renato Tinós

• Advisor: Fernando José Von Zuben

Abstract

The dynamism of the modern world gives rise to huge scientific and technological challenges. Problems until recently being treated as static are now being reformulated to incorporate that dynamism, thus requiring novel solution strategies. Population-based metaheuristics devoted to optimization emerge as promising approaches, given that they promote an effective exploration of the search space and contribute to the adaptation to the dynamism of the environment. Estimation of distribution algorithms (EDAs) were considered here, which make use of probabilistic models to identify promising regions of the search space. Due to the fact that the proposals of EDAs for dynamic problems are rare and limited, mainly in real-parameter search spaces, EDAs were conceived based on flexible Gaussian mixture models, self-controlable and computationally inexpensive steps, including diversity maintenance and convergence control mechanisms. An extensive comparison with alternative optimization methods for dynamic environments was accomplished and, in many situations, the proposed technique overcame the performance produced by state-of-the-art methods (AU)

• FAPESP's process: 09/06757-0 - Dynamic optimization employing estimation of distribution models in continuous search spaces
• Grantee: André Ricardo Gonçalves
• Support Opportunities: Scholarships in Brazil - Master