Evolutionary algorithms for constrained engineering problems
Computers and Industrial Engineering
Evolutionary Computation at the Edge of Feasibility
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
On three new approaches to handle constraints within evolution strategies
Natural Computing: an international journal
Evolving content in the galactic arms race video game
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Revising the evolutionary computation abstraction: minimal criteria novelty search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Constraint-handling techniques used with evolutionary algorithms
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Interactive evolution for the procedural generation of tracks in a high-end racing game
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 9th conference on Computing Frontiers
Generating map sketches for strategy games
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Novelty search is a recent algorithm geared to explore search spaces without regard to objectives; minimal criteria novelty search is a variant of this algorithm for constrained search spaces. For large search spaces with multiple constraints, however, it is hard to find a set of feasible individuals that is both large and diverse. In this paper, we present two new methods of novelty search for constrained spaces, Feasible-Infeasible Novelty Search and Feasible-Infeasible Dual Novelty Search. Both algorithms keep separate populations of feasible and infeasible individuals, inspired by the FI-2pop genetic algorithm. These algorithms are applied to the problem of creating diverse and feasible game levels, representative of a large class of important problems in procedural content generation for games. Results show that the new algorithms under certain conditions can produce larger and more diverse sets of feasible strategy game maps than existing algorithms. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. It is also shown that the proposed enhancement of offspring boosting increases performance in all cases.