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Parallel genetic algorithm for search and constrained multi-objective optimization

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4 Author(s)
Wilson, L.A. ; Comput. & Math. Sci., Texas A&M Univ., TX, USA ; Moore, M.D. ; Picarazzi, J.P. ; Miquel, S.D.S.

Summary form only given. Parallel genetic algorithm for search and constrained multiobjective optimization introduces the design and complexity analysis of a parallel genetic algorithm to generate a "best" path for a robot arm to follow, given a starting position and a goal in three dimensional space. Path generation takes into account any obstacles near the arm. This algorithm uses multiple optimization criteria, independent cross-pollinating populations, and handles multiple hard constraints. Individuals in the population consist of multiple chromosomes. The complexity of the algorithm is the number of generations processed times O(N ) where N is the total number of individuals used for path generation on all of the optimizations.

Published in:

Parallel and Distributed Processing Symposium, 2004. Proceedings. 18th International

Date of Conference:

26-30 April 2004