Towards Directed Open-Ended Search by a Novelty Guided Evolution Strategy
In the conceptional phases of design optimization tasks it is required to find new innovative solutions to a given problem. Although evolutionary algorithms are suitable methods to this problem, the search of a wide range of the solution space in order to identify novel concepts is mainly driven by random processes and is therefore a demanding task, especially for high dimensional problems. To improve the exploration of the design space additional criteria are proposed in the presented work which do not evaluate solely the quality of a solution but give an estimation of the probability to find alternative optima. To realize these criteria, concepts of novelty and interestingness are employed. Experiments on test functions show that these novelty guided evolution strategies identify multiple optima and demonstrate a switching between states of exploration and exploitation. With this we are able to provide first steps towards an alternative search algorithm for multi-modal functions and the search during conceptual design phases.
KeywordsEvolutionary algorithm open-endedness interestingness multi-objective optimization novelty detection prediction error niching
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