Simulated Evolution under Multiple Criteria Conditions Revisited

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.