1,074 research outputs found
Evidence of coevolution in multi-objective evolutionary algorithms
This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are made in what solutions are able to interact during the ranking process and 2) evolution takes place in a multi-objective environment. This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered in the literature. Second, it demonstrates that the preconditions for coevolutionary behavior are weaker than previously thought. In particular, our model indicates that direct cooperation or competition between species is not required for coevolution to take place. Moreover, our experiments provide evidence that environmental perturbations can drive coevolutionary processes; a conclusion that mirrors arguments put forth in dual phase evolution theory. In the discussion, we briefly consider how our results may shed light onto this and other recent theories of evolution
A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
Large-scale optimization problems that involve thousands of decision
variables have extensively arisen from various industrial areas. As a powerful
optimization tool for many real-world applications, evolutionary algorithms
(EAs) fail to solve the emerging large-scale problems both effectively and
efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA
that can not only produce high-quality solution by solving sub-problems
separately, but also highly utilizes the power of parallel computing by solving
the sub-problems simultaneously. Existing DC-based EAs that were deemed to
enjoy the same advantages of the proposed algorithm, are shown to be
practically incompatible with the parallel computing scheme, unless some
trade-offs are made by compromising the solution quality.Comment: 12 pages, 0 figure
Evolution of interactions and cooperation in the spatial prisoner's dilemma game
We study the evolution of cooperation in the spatial prisoner's dilemma game
where players are allowed to establish new interactions with others. By
employing a simple coevolutionary rule entailing only two crucial parameters,
we find that different selection criteria for the new interaction partners as
well as their number vitally affect the outcome of the game. The resolution of
the social dilemma is most probable if the selection favors more successful
players and if their maximally attainable number is restricted. While the
preferential selection of the best players promotes cooperation irrespective of
game parametrization, the optimal number of new interactions depends somewhat
on the temptation to defect. Our findings reveal that the "making of new
friends" may be an important activity for the successful evolution of
cooperation, but also that partners must be selected carefully and their number
limited.Comment: 14 pages, 6 figures; accepted for publication in PLoS ON
Coevolutionary architectures with straight line programs for solving the symbolic regression problem
This is an electronic version of the paper presented at the International Conference on Evolutionary Computation (ICEC), held in Valencia (Spain) on 2010To successfully apply evolutionary algorithms to the solution of increasingly complex problems we must develop
effective techniques for evolving solutions in the form of interacting coadapted subcomponents. In this
paper we present an architecture which involves cooperative coevolution of two subcomponents: a genetic program
and an evolution strategy. As main difference with work previously done, our genetic program evolves
straight line programs representing functional expressions, instead of tree structures. The evolution strategy
searches for good values for the numerical terminal symbols used by those expressions. Experimentation has
been performed over symbolic regression problem instances and the obtained results have been compared
with those obtained by means of Genetic Programming strategies without coevolution. The results show that
our coevolutionary architecture with straight line programs is capable to obtain better quality individuals than
traditional genetic programming using the same amount of computational effort.This work is partially supported by spanish grants TIN2007-67466-C02-02, MTM2004-01167 and S2009/TIC-165
Using Localised ‘Gossip’ to Structure Distributed Learning
The idea of a “memetic” spread of solutions through a human culture in parallel to their development is applied as a distributed approach to learning. Local parts of a problem are associated with a set of overlappingt localities in a space and solutions are then evolved in those localites. Good solutions are not only crossed with others to search for better solutions but also they propogate across the areas of the problem space where they are relatively successful. Thus the whole population co-evolves solutions with the domains in which they are found to work. This approach is compared to the equivalent global evolutionary computation approach with respect to predicting the occcurence of heart disease in the Cleveland data set. It greatly outperforms the global approach, but the space of attributes within which this evolutionary process occurs can effect its efficiency
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
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