s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

This paper deals with the optimization of shape placement, called marker making in suit make up industry. It consists in placing a set of irregular shaped apparel patterns on a limited material surface without overlapping and with minimum waste. We describe a new approach by combining tree simulated annealing (TSA) and genetic algorithm (GA). The first method integrates stochastic features of simulated annealing to tree search in order to both reduce the memory space and guarantee a good solution. The second method takes benefit of parallel aspect of the search strategy in genetic algorithms by evolving a population of solution candidates, instead of a single one in simulated annealing. We cannot judge which of the two methods is better, that will depend on practical context: if the marker making process works in real time, the sequential search method may be preferable to provide a marker at a time, else genetic-like algorithm may be used for off-line run. INTRODUCTION The optimizat...

A computer based simulation with artificial adaptive agents for predicting secondary structure from the protein hydrophobicity ffl [34] Accurate Gen. Lander: An Experiment in ffl NeuroGenetic Cntr. [149] adaptation Characterizing the ffl abilities of a class of gen. based machine learning alg. [45] ffl in dynamic environments through a minimal probability of exploration [125] adaptive A computer based simulation with artificial ffl agents for predicting secondary structure from the protein hydrophobicity [Abstract] [15] -- A hierarchical classifier syst. implementing a motivationally autonomous ffl animat [40] -- Computer simulations of ffl behavior in animats [98] -- Darwinian ffl simulated annealing [99] -- Extended classifiers for simulation of ffl behavior [114] -- From animals to animats: everything you wanted to know about the simulation of ffl behaviour [19] -- Fuzzy Q-learning and evol. strategy for ffl fuzzy cntr. [115] -- Simulation of ffl behavior in animats: Review and pr...