Abstract
Evolutionary Algorithms, inspired from the Darwinian theory on evolution of species, are heuristic method for solving difficult unimodal and multimodal functions. But the ultimate disadvantage of those Evolutionary Algorithms is premature convergence, i.e. trapping in a local optimum due to poor exploration strategy. In case of High Dimensional problems, there are huge chances of convergence prematurely due to the large search space, which grows exponentially with the increase of dimension of the problem. In this paper a modified Teaching-Learning-Based technique is used to investigate the effectiveness of different cooperative co-evolutionary framework for solving high dimensional problems.
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Biswas, S., Kundu, S., Bose, D., Das, S. (2012). Cooperative Co-evolutionary Teaching-Learning Based Algorithm with a Modified Exploration Strategy for Large Scale Global Optimization. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_55
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DOI: https://doi.org/10.1007/978-3-642-35380-2_55
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