Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
This work addresses the issue of maintaining diversity in evolutionary algorithms for researchers and practitioners, but it appears incremental as it builds on existing concepts without claiming major breakthroughs.
The paper tackled the problem of premature convergence in evolutionary algorithms by analyzing and introducing diversity measures that explicitly influence fitness without requiring domain-specific knowledge, showing these approaches can help in global optimization.
Diversity is an important factor in evolutionary algorithms to prevent premature convergence towards a single local optimum. In order to maintain diversity throughout the process of evolution, various means exist in literature. We analyze approaches to diversity that (a) have an explicit and quantifiable influence on fitness at the individual level and (b) require no (or very little) additional domain knowledge such as domain-specific distance functions. We also introduce the concept of genealogical diversity in a broader study. We show that employing these approaches can help evolutionary algorithms for global optimization in many cases.