Genetic algorithms represent a class of adaptive search techniques that have been intensively studied in recent years. Much of the interest in genetic algorithms is due to the fact that they provide a set of efficient domain-independent search heuristics which are a significant improvement over traditional "weak methods" without the need for incorporating highly doinain-specific knowledge. There is now considerable evidence that genetic algorithms are usefifl for global flmction optimization and NP-hard problems. Recently, there has been a good deal of interest in using genetic algorithms for machine learning problems. This paper provides a brief overview of how one might use genetic algorithms as a key element in learning systems.

Summary The interaction between thrust and strike slip fault systems is well detailed in Pakistan where the Chaman transform zone connects the Makran and Himalayan convergence zones and contains an internal convergence zone in the Zhob district. The transform zone contains numerous strike slip faults of which the Chaman fault proper is the westernmost. We can demonstrate at least 200 km of left lateral displacement along the Chaman fault alone. In the Zhob belt N-S shortening by folds and a major thrust fault amounts to several dozen kilometres. The 400 km wide Makran convergence zone is now being shortened by E-W oriented folds, thrust faults, and reverse faults. As these faults in the Makran zone approach the transform zone, their traces bend to the N and motion on each of them becomes oblique, combining reverse and left lateral slip. They merge continuously with the strike slip faults of the Chaman transform zone. The Makran thrust system and the Chaman transform zone first became active in the late Oligocene or early Miocene. Later (Pliocene?), a component of left lateral shear occurred across the entire Makran Zone in association with the opening of the newly identified Haman-i-Mashkel fault trough S of the Chagai Hills and W of the Ras Koh. The total displacement and displacement rate across the Chaman transform zone varies in response to the rates of convergence in the plates E and W of the zone.

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Abstract. This paper presents a variation of the Euclidean Traveling Salesman Problem (TSP), the Multiple Traveling Salesman Problem (MTSP), and compares a variety of evolutionary computation algorithms and paradigms for solving it. Techniques implemented, analyzed, and discussed herein with regard to MTSP include use of a neighborhood attractor schema (a variation on k-means clustering), the "shrink-wrap" algorithm for local neighborhood optimization, particle swarm optimization, Monte-Carlo optimization, and a range of genetic algorithms and evolutionary strategies.

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