Genetic algorithms overview

A brief overview of different types of molecular descriptors is given in Chapter 9 about cell-based partitioning by Xue et al. this chapter also includes a description of genetic algorithm calculations. 

SIMCA and related methods Back propagation neural networks Decision trees Genetic algorithms Pattern recognition in data sets A Overview... 

T. Back, F. Hoffmeister, and H.-P. Schwefel, in Proceedings of the Fourth International Conference on Genetic Algorithms, R. K. Belew and L. B. Booker, Eds., Morgan Kaufmann, San Mateo, CA, 1991, pp. 2-9. A Survey of Evolutionary Strategies. T. Back and H.-P. Schwefel, Evolutionary Computation, 1, 1 (1993). An Overview of Evolutionary Algorithms for Parameter Optimization. 

As an alternative to RSM, simulation responses can be used directly to explore the sample space of control variables. To do so, a lot of combinatorial optimization approaches were adapted for simulation optimization. In general, there are four main classes of methods that have shown a particular applicability in (multi-objective) simulation optimization Meta-heuristics, gradient-based procedures, random search, and sample path optimization. Of particular interest are meta-heuristics as they have shown a good performance for a wide range of combinatorial optimization approaches. Therefore, commercial simulation software primarily uses these techniques to incorporate simulation optimization routines. Among meta-heuristics, tabu search, scatter search, and genetic algorithms are most widely used. Table 4.13 provides an overview on aU aforementioned techniques. 

De Jong K (1988) Learning with genetic algorithms an overview. Mach Learn 3(2-3) 121-138... 

Beasley, D., Bull, D.R., Martin, R.R. (1993a), An overview of genetic algorithms Part 1 Fundamentals, University of Waies, College of Cardiff, University Computing, Cardiff, Wales, 15 (2), pp. 58-69... 

Halperin, 1., Ma, B., Wolfson, H., and Nussinov, R. Principles of docking an overview of search algorithms and a guide to scoring functions. Proteins Struct., Fund., Genet. 2002, 47,... 

Also, reviews of the current state of the art in the application of evolutionary methods to problems of interest to computational chemists have been compiled. In this article, we provide an overview of GAs, how to develop and implement them, elaborating on conunon practices across applications. We also discuss two applications to problems of relevance to computational chemistry in some detail. The article attempts to provide the reader with a primer in GAs from an application point of view and pointers to areas in the literature for other extensions, when appropriate. This article is not meant to be a review. The reader interested in a comprehensive review of past and current research in GA applications, is referred to Genetic and Evolutionary Algorithms. 

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