A genetic approach to the quadratic assignment problem
Computers and Operations Research - Special issue on genetic algorithms
How to solve it: modern heuristics
How to solve it: modern heuristics
Shall We Repair? Genetic AlgorithmsCombinatorial Optimizationand Feasibility Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
Repair and Brood Selection in the Traveling Salesman Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
AE '95 Selected Papers from the European conference on Artificial Evolution
Genetic Repair for Optimization under Constraints Inspired by Arabidopsis Thaliana
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Biologically inspired non-mendelian repair for constraint handling in evolutionary algorithms
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Genetic repair strategies inspired by Arabidopsis thaliana
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
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Handling constraints for combinatorial optimization problems is a classic challenge faced by genetic and evolutionary algorithms. This paper explores a naturally inspired genetic repair process to enforce constraints on evolutionary search. Lolle et al. (2005) controversially claim that the model plant Arabidopsis thaliana appears to repair genetic errors using information inherited from ancestors other than the immediate parents [10] (i.e. non-Mendelian inheritance). We adapt this natural template-driven genetic repair process (GeneRepair) to help solve constraint problems. Building upon previous results [6][7][8] this paper explores repair templates that originate across a range of ancestors, between one and many thousands of generations old. The fitness of resulting populations are presented and compared to a benchmark technique using a random repair template [9]. The results show that very ancient (ancestral) repair templates perform best for larger problems, significantly outperforming the benchmark. The impact of background mutation rates on solution quality is also explored. Results suggest that ancestral repair is a good general-purpose constraint handling technique - helping to explain why this strategy might have evolved in nature.