Memetic Algorithms for molecular Conformation and other optimization problems

this paper, based on the generalized tight-binding molecular dynamics, we apply the GA to study the surface reconstruction of Silicon (001) for the first time". Hybrid population approaches like this can hardly be catalogues as being `genetic', but this denomination has appeared in previous work by Deaven and Ho [5] which got extra recognition due to an article by J. Maddox in Nature [24]. Other applications include cluster physics. Niesse and Mayne in [29] said: "In a recent paper, Gregurick, Alexander, and Hartke [S. K. Gregurick, M. H. Alexander, and B. Hartke, J. Chem. Phys. 104, 2684 (1996)] proposed a global geometry optimization technique using a modified Genetic Algorithm approach for clusters. They refer to their technique as a deterministic/stochastic genetic algorithm (DS-GA). In this technique, the stochastic part is a traditional GA, with the manipulations being carried out on binary-coded internal coordinates (atom-atom distances). The deterministic aspect of their method is the inclusion of a coarse gradient descent calculation on each geometry. This step avoids spending a large amount of computer time searching parts of the configuration space which correspond to high-energy geometries. Their tests of the technique show it is vastly more efficient than searches without this local minimization". Other papers in the area of cluster physics include [30] [16] [31] [37] [15] [6]. Other evolutionary approaches to a variety of molecular problems can be found in: [36] [32] [8] [26] [25] [14] [13]. Their use for design problems is particularly appealing, see: [18] [38] [2]. They have also found a way into protein design [7] [20], probably due to their landscape structure [28]. Reference