Scheduling a casting sequence involving a number of orders with dierent casting weights and satisfying due dates of production is an important optimization problem often encountered in foundries. In this paper, we attempt to solve this complex, multi-variable, and multiconstraint optimization problem using different implementations of genetic algorithms (GAs). In

A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary principles and chromosomal processing in natural genetics. A GA begins its search with a random set of solutions usually coded in binary string structures. Every solution is assigned a fitness which is directly related to the objective function of the search and optimization problem. Thereafter, the population of solutions is modified to a new population by applying three operators similar to natural genetic operators---reproduction, crossover, and mutation. A GA works iteratively by successively applying these three operators in each generation till a termination criterion is satisfied. Over the past one decade, GAs have been successfully applied to a wide variety of problems, because of their simplicity, global perspective, and inherent parallel processing. In this paper, we outline the working principle of a GA by describing these three operators and by outlining an intuitive sketch ...

Abstract — This research presents an investigation into a new population inheritance approach using a concept taken from the recessive trait idea for evolutionary optimization. Evolutionary human inheritance recessive trait idea is used to enhance the effectiveness of the traditional genetic algorithms. The capability of the modified approach is explored by two examples (i) a mathematical function of two variables, and (ii) an active vibration control of a flexible beam system. Finally, a comparative performance for convergence is presented and discussed to demonstrate the merits of the modified genetic algorithms approach over the traditional ones.

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There are many people I would like to thank for their support. Principally I thank my family, Sue, Nick, Katie and William, and my Supervisor Larry Bull. All have shown tolerance, forbearance, support and inspiration well beyond the call of duty. In addition Dave Wyatt and Praminda Caleb-Solly have been very knowledgeable, approachable and helpful, I owe them a lot. The UWE LCSG has been a key source of ideas, knowledge and inspiration. Books and papers cannot provide such a level of understanding and debate about fundamental issues. Amongst the key players Larry Bull, Alwyn Barry, Dave Wyatt, Matt Studley, Chris Stone, Tony Pipe, Brian Carse and Rob Smith have always provided difficult and thought provoking questions, and sometimes even answers. Lastly thanks to Paul Lewis, Joe Mackenzie both of BT, Terry Fogarty, and Roger Miles who in there own ways facilitated the move from being a competent project manager to entering the (from Huxley) “brave new world that hath such people in’t ” of evolutionary and neural computing. This thesis investigates a hybrid of evolutionary computing and neural computing which long has been a goal of machine learning. X-NCS is a neural and hence a more complex version of XCS (Wilson 1995), the pre-eminent accuracy based Learning Classifier System (LCS) (Holland, 1986). XCS differs from other