A Cooperative Coevolutionary Approach to Function Optimization

By Mitchell A. Potter, Kenneth De Jong. 1994

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Citations in the corpus

Action Semantics Network: Considering the Effects of Actions in Multiagent Systems (2019, background): “More recently, Palmer et al. (2018) extended the idea of leniency (Potter & Jong, 1994; Panait et al., 2008) to deep MARL and proposed the retroactive temperature decay schedule to address stochastic rewards problems.”
Lenient Multi-Agent Deep Reinforcement Learning (2017, methodology): “Lenient learning was originally introduced by Potter and De Jong [20] to help cooperative co-evolutionary algorithms converge towards an optimal policy.”
Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization (2014, methodology): “In the original implementation of the cooperative coevolutionary genetic algorithm (CCGA), Potter and De Jong [10] decomposed an n-dimensional problem into n 1-D problems.”
Self-adaptive learning based particle swarm optimization (2011, background): “In [42], Potter and De Jong introduced cooperative co-evolution (CC) into EAs.”
Multilevel cooperative coevolution for large scale optimization (2008, methodology): “Initial efforts in tackling high-dimensional problems used two simple problem decomposition methods, i.e., the one-dimensional based and splitting-in-half strategies [6], [8], [9].”
Parallel Implementation of a Random Search Procedure: An Experimental Study (2005, methodology): “Together with other global optimization techniques [1, 2, 3, 4], including Simulated Annealing, evolutionary algorithms, and the Tunneling method, random search presents a powerful approach to solving discrete optimization problems when the objective function is too complex to obtain the solution an”
Lenient learners in cooperative multiagent systems (2006, background): “Cooperative coevolutionary systems [6] are variants of evolutionary computation (a stochastic optimization technique) which apply multiple parallel learners (optimization processes) to work jointly on different aspects of the problem.”
A survey on optimization metaheuristics (2013, methodology): “A general framework for cooperative coevolutionary algorithms has been introduced by Potter and De Jong [213] in 1994 for evolving solutions in the form of co-adapted subcomponents.”
An analysis of particle swarm optimizers (2002, methodology): “The original CCGA-1 algorithm, due to Potter [106, 105], suffers from the same problem, although Potter did not identify the problem as such.”
A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables (2016, methodology): “Inspired by the cooperative coevolution [6]–[9] and linkage learning methods [10], [11],”
A Study of the Parallelization of a Coevolutionary Multi-objective Evolutionary Algorithm (2004, background): “Evolutionary computation researchers have developed several coevolutionary approaches in which normally two or more species relate to each other using any of the possible relationships, mainly competitive (e.g., [5]) or cooperative (e.g., [ 6 ]) relationships.”
Coevolutionary Computation (1995, background): “The work described in [15] is relevant with respect to the symbiotic CGA.”
A Comprehensive Survey of Multiagent Reinforcement Learning (2008, background): “interested reader, examples of co-evolution techniques, w here the behaviors of the agents evolve in parallel, can be found i n [18]–[20].”