Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Contemporary Evolution Strategies
Proceedings of the Third European Conference on Advances in Artificial Life
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Evolutionary Search for Minimal Elements in Partially Ordered Finite Sets
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Using predators and preys in evolution strategies
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Using gene deletion and gene duplication in evolution strategies
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
On three new approaches to handle constraints within evolution strategies
Natural Computing: an international journal
Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Designing multi-objective variation operators using a predator-prey approach
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
On the Evolutionary Optimization of Many Conflicting Objectives
IEEE Transactions on Evolutionary Computation
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
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Evolutionary Algorithms (EAs) as one important subdomain of Computational Intelligence (CI) have conquered the field of experimental as well as difficult numerical optimization despite the lack of addresses of welcome half a century ago. Meanwhile, they go without saying into the toolboxes of most practitioners who have to solve real-world problems. And an overwhelming number of theoretical results underpin at least parts of the practice. More recently, even vector optimization problems can be tackled by means of specialized EAs. These multiobjective evolutionary algorithms (MOEAs or EMOAs) help decision makers to reduce the number of design possibilities to the subsets that make the best of the situation in case of conflicting objectives. This article briefly describes the problem setting, the most important solution approaches, and the challenges that still lie ahead in their improvement. Most sophisticated algorithms in this domain have somehow lost their character of mimicking natural mechanisms found in organic evolution. That is why a couple of more bio-inspired aspects are mentioned in the second part of this contribution that may help to diversify further research and practice in multiobjective optimization (MOO) without forgetting to foster the interdisciplinary dialogue with natural scientists.