Abstract
Evolutionary algorithms (EAs) have been applied to many optimisation problems successfully in recent years. The genetic algorithm (GA) and evolutionary programming (EP) are two of the major branches of EAs. GAs use crossover as the main search operator and mutation as a background operator in search. EP typically uses mutation only. This paper investigates a novel EP algorithm for cutting stock problems. It adopts a mutation operator based on the concept of distance between a parent and its offspring. Without using crossover, the algorithm is less time consuming and more efficient in comparison with a GA-based approach. Experimental studies have been carried out to examine the effectiveness of the EP algorithm. They illustrate that EP can provide a simple yet more efficient alternative to GAs in solving some combinatorial optimisation problems.KeywordsMutation OperatorCrossover OperatorCombinatorial Optimisation ProblemFinite State MachineFitness Evaluation FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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