Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Hard and easy distributions of SAT problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Defining and optimizing indicator-based diversity measures in multiobjective search
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A bi-criterion approach to multimodal optimization: self-adaptive approach
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Introduction to Evolutionary Algorithms
Introduction to Evolutionary Algorithms
Multiobjectivization with NSGA-ii on the noiseless BBOB testbed
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Diversely enumerating system-level architectures
Proceedings of the Eleventh ACM International Conference on Embedded Software
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Typically, optimization attempts to find a solution which minimizes the given objective function. But often, it might also be useful to obtain a set of structurally very diverse solutions which all have acceptable objective values. With such a set, a decision maker would be given a choice of solutions to select from. In addition, he can learn about the optimization problem at hand by inspecting the diverse close-to-optimal solutions. This paper proposes NOAH, an evolutionary algorithm which solves a mixed multi-objective problem: Determine a maximally diverse set of solutions whose objective values are below a provided objective barrier. It does so by iteratively switching between objective value and set-diversity optimization while automatically adapting a constraint on the objective value until it reaches the barrier. Tests on an nk-Landscapes problem and a 3-Sat problem as well as on a more realistic bridge construction problem show that the algorithm is able to produce high quality solutions with a significantly higher structural diversity than standard evolutionary algorithms.