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A Kernighan-Lin Local Improvement Heuristic That Solves Some Hard Problems in Genetic Algorithms
William A. Greene
Computer Science Department
University of New Orleans
New Orleans, LA 70148, USA
bill@cs.uno.edu
Abstract. We present a Kernighan-Lin style local improvement heuristic for genetic algorithms. We analyze the run-time cost of the heuristic. We demonstrate through experiments that the heuristic provides very quick solutions to several problems which have been touted in the literature as especially hard ones for genetic algorithms, such as hierarchical deceptive problems. We suggest why the heuristic works well.
LNCS 2724, p. 1582 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003