منابع مشابه
Novelty-Driven Cooperative Coevolution
Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs enable, for instance, the evolution of cooperative multiagent systems composed of heterogeneous agents, where each agent is modelled as a component of the solution. Previous works have, however, shown that CCEAs are biased toward stability:...
متن کاملAvoiding convergence in cooperative coevolution with novelty search
Cooperative coevolution is an approach for evolving solutions composed of coadapted components. Previous research has shown, however, that cooperative coevolutionary algorithms are biased towards stability: they tend to converge prematurely to equilibrium states, instead of converging to optimal or near-optimal solutions. In single-population evolutionary algorithms, novelty search has been sho...
متن کاملNovelty Search in Competitive Coevolution
One of the main motivations for the use of competitive coevolution systems is their ability to capitalise on arms races between competing species to evolve increasingly sophisticated solutions. Such arms races can, however, be hard to sustain, and it has been shown that the competing species often converge prematurely to certain classes of behaviours. In this paper, we investigate if and how no...
متن کاملA benchmark for cooperative coevolution
Cooperative co-evolution algorithms (CCEA) are a thriving sub-field of evolutionary computation. This class of algorithms makes it possible to exploit more efficiently the artificial Darwinist scheme, as soon as an optimisation problem can be turned into a co-evolution of interdependent sub-parts of the searched solution. Testing the efficiency of new CCEA concepts, however, it is not straightf...
متن کاملNovelty-Driven Particle Swarm Optimization
Particle Swarm Optimization (PSO) is a well-known population-based optimization algorithm. Most often it is applied to optimize objective-based fitness functions that reward progress towards a desired objective or behavior. As a result, search increasingly focuses on higherfitness areas. However, in problems with many local optima, such focus often leads to premature convergence that precludes ...
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ژورنال
عنوان ژورنال: Evolutionary Computation
سال: 2017
ISSN: 1063-6560,1530-9304
DOI: 10.1162/evco_a_00173