The MaxSolve algorithm for coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Monotonic solution concepts in coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On identifying global optima in cooperative coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
DECA: dimension extracting coevolutionary algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A game-theoretic investigation of selection methods in two-population coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Coevolution of neural networks using a layered pareto archive
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The parallel Nash Memory for asymmetric games
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Robustness in cooperative coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A novel method for automatic strategy acquisition in N-player non-zero-sum games
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Human evolutionary model: A new approach to optimization
Information Sciences: an International Journal
A Monotonic Archive for Pareto-Coevolution
Evolutionary Computation
Introductory tutorial on coevolution
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A comparison of evaluation methods in coevolution
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Combining Simulation and Reality in Evolutionary Robotics
Journal of Intelligent and Robotic Systems
Coevolving programs and unit tests from their specification
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
A no-free-lunch framework for coevolution
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evaluation and Diversity in Co-evolution
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Black-box search by elimination of fitness functions
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Unbiased coevolutionary solution concepts
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Monotonicity versus performance in co-optimization
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Why Coevolution Doesn't "Work": Superiority and Progress in Coevolution
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
A co-evolutionary approach for military operational analysis
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Analysis of coevolution for worst-case optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolutionary game theoretic approach for modeling civil violence
IEEE Transactions on Evolutionary Computation
Coevolutionary temporal difference learning for Othello
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
A solution concept for artificial immune networks: a coevolutionary perspective
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Evolutionary mechanism design: a review
Autonomous Agents and Multi-Agent Systems
Theoretical convergence guarantees for cooperative coevolutionary algorithms
Evolutionary Computation
On the practicality of optimal output mechanisms for co-optimization algorithms
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Learning n-tuple networks for othello by coevolutionary gradient search
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Approximating n-player behavioural strategy nash equilibria using coevolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Co-evolution of optimal agents for the alternating offers bargaining game
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Evolving small-board Go players using coevolutionary temporal difference learning with archives
International Journal of Applied Mathematics and Computer Science
The effects of diversity maintenance on coevolution for an intransitive numbers problem
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
A multimodal problem for competitive coevolution
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Semantic bias in program coevolution
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Improving coevolution by random sampling
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A survey on optimization metaheuristics
Information Sciences: an International Journal
Co-evolutionary automatic programming for software development
Information Sciences: an International Journal
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Inspired by the principle of natural selection, coevolutionary algorithms are search methods in which processes of mutual adaptation occur amongst agents that interact strategically. The outcomes of interaction reveal a reward structure that guides evolution towards the discovery of increasingly adaptive behaviors. Thus, coevolutionary algorithms are often used to search for optimal agent behaviors in domains of strategic interaction. Coevolutionary algorithms require little a priori knowledge about the domain. We assume the learning task necessitates the algorithm to (1) discover agent behaviors, (2) learn the domain's reward structure, and (3) approximate an optimal solution. Despite the many successes of coevolutionary optimization, the practitioner frequently observes a gap between the properties that actually confer agent adaptivity and those expected (or desired) to yield adaptivity, or optimality. This gap is manifested by a variety of well-known pathologies, such as cyclic dynamics, loss of fitness gradient, and evolutionary forgetting. This dissertation examines the divergence between expectation and actuality in co-evolutionary algorithms—why selection pressures fail to conform to our beliefs about adaptiveness, or why our beliefs are evidently erroneous. When we confront the pathologies of coevolutionary algorithms as a collection, we find that they are essentially epiphenomena of a single fundamental problem, namely a lack of rigor in our solution concepts . A solution concept is a formalism with which to describe and understand the incentive structures of agents that interact strategically. All coevolutionary algorithms implement some solution concept, whether by design or by accident, and optimize according to it. Failures to obtain the desiderata of “complexity” or “optimality” often indicate a dissonance between the implemented solution concept and that required by our envisaged goal. We make the following contributions: (1) We show that solution concepts are the critical link between our expectations of coevolution and the outcomes actually delivered by algorithm operation, and are therefore crucial to explicating the divergence between the two, (2) We provide analytic results that show how solution concepts bring our expectations in line with algorithmic reality, and (3) We show how solution concepts empower us to construct algorithms that operate more in line with our goals.