Biologically Inspired Non-Mendelian Repair for Constraint Handling in Evolutionary Algorithms

This paper examines a repair technique that enables evolutionary algorithms to handle constraints. This repair technique, known as GeneRepair, repairs invalid individuals so that all problem constraints are met by every individual in the population. GeneRepair is based on the repair technique used by the Arabidopsis thaliana plant which was proposed by Lolle et al in 2005. This controversial repair method uses information inherited from ancestors previous to the parent (non-Mendelian inheritance) as a repair template to fix errors or invalidities in the current population. We compare the use of three different ancestors as repair templates and investigate the effects of various biological parameters on the choice of repair template to use

Evolutionary computation applied to combinatorial optimisation problems

This thesis addresses the issues associated with conventional genetic algorithms (GA) when applied to hard optimisation problems. In particular it examines the problem of selecting and implementing appropriate genetic operators in order to meet the validity constraints for constrained optimisation problems. The problem selected is the travelling salesman problem (TSP), a well known NP-hard problem. Following a review of conventional genetic algorithms, this thesis advocates the use of a repair technique for genetic algorithms: GeneRepair. We evaluate the effectiveness of this operator against a wide range of benchmark problems and compare these results with conventional genetic algorithm approaches. A comparison between GeneRepair and the conventional GA approaches is made in two forms: firstly a handcrafted approach compares GAs without repair against those using GeneRepair. A second automated approach is then presented. This meta-genetic algorithm examines different configurations of operators and parameters. Through the use of a cost/benefit (Quality-Time Tradeoff) function, the user can balance the computational effort against the quality of the solution and thus allow the user to specify exactly what the cost benefit point should be for the search. Results have identified the optimal configuration settings for solving selected TSP problems. These results show that GeneRepair when used consistently generates very good TSP solutions for 50, 70 and 100 city problems. GeneRepair assists in finding TSP solutions in an extremely efficient manner, in both time and number of evaluations required