A review of landmark articles in the field of co-evolutionary computing
Noe Casas

TL;DR
This paper reviews key landmark articles in co-evolutionary computing, highlighting their techniques and contributions to improving evolutionary algorithms since 1990.
Contribution
It provides a comprehensive overview of significant co-evolutionary methods and their evolution over time, emphasizing their role in enhancing robustness.
Findings
Identifies foundational techniques in co-evolutionary computing
Highlights improvements in avoiding local optima
Shows evolution of algorithms since 1990
Abstract
Coevolution is a powerful tool in evolutionary computing that mitigates some of its endemic problems, namely stagnation in local optima and lack of convergence in high dimensionality problems. Since its inception in 1990, there are multiple articles that have contributed greatly to the development and improvement of the coevolutionary techniques. In this report we review some of those landmark articles dwelving in the techniques they propose and how they fit to conform robust evolutionary algorithms
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Reinforcement Learning in Robotics
