Psychologically Motivated Clonal Algorithm based Approach to Solve Planning Problem
نویسنده
چکیده
Planning is a subject of interest to the Artificial Intelligence community. Genetic algorithms, neural networks, and simulated annealing are heuristic search methods often used to solve complex optimization problems. In this paper, we have proposed a novel intelligence paradigm to solve planning problems. This paper extends the Artificial Immune System (AIS) approach by proposing a new methodology termed as Psychologically Motivated Clonal Algorithm (PMCA). AIS approach is amalgamated with motivational theories to evolve a robust meta-heuristic. We have reported results for a planning problem related to a manufacturing system and compared these results with Genetic Algorithm. The results obtained have a significant improvement over the GA.
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