NDLI: Problem decomposition using indirect reciprocity in evolved populations

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Parallel shared memory strategies for ant-based optimization algorithms

Evolution of functional specialization in a morphologically homogeneous robot

Evolutionary hypernetwork classifiers for protein-proteininteraction sentence filtering

Improved analysis methods for crossover-based algorithms

On empirical memory design, faster selection of bayesian factorizations and parameter-free gaussian EDAs

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Adaptation of expansion rate for real-coded crossovers

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Geometric differential evolution

Limiting the velocity in particle swarm optimization using a geometric series

On the characteristics of sequential decision problems and their impact on evolutionary computation

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Competitive simulated annealing and Tabu Search algorithms for the max-cut problem

Estimation of particle swarm distribution algorithms: bringing together the strengths of PSO and EDAs

Modulation of multi-level evolutionary strategies for artificial cognition

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Distributed hyper-heuristics for real parameter optimization

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MC/DC automatic test input data generation

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Design & Implementation of Real-time Parallel GA Operators on the IBM Cell Processor

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Problem decomposition using indirect reciprocity in evolved populations

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EDA-RL: estimation of distribution algorithms for reinforcement learning problems

On strategy parameter control by Meta-ES

Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences

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Bayesian network structure learning using cooperative coevolution

A genetic programming approach to automated software repair

Learning sensorimotor control structures with XCSF: redundancy exploitation and dynamic control

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Evolutionary algorithms for the mapping of pipelined applications onto heterogeneous embedded systems

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Single step evolution of robot controllers for sequential tasks

Computer simulation on the maternal effect dominant embryonic arrest (MEDEA) for disease vector population replacement

Metaheuristics for graph bisection

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When and why development is needed: generative and developmental systems

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Towards identifying salient patterns in genetic programming individuals

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The bee colony-inspired algorithm (BCiA): a two-stage approach for solving the vehicle routing problem with time windows

Sustaining diversity using behavioral information distance

Multiobjectivization for parameter estimation: a case-study on the segment polarity network of drosophila

An experimental investigation of model-based parameter optimisation: SPO and beyond

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Combining evolution strategy and gradient descent method for discriminative learning of bayesian classifiers

Multiplicative approximations and the hypervolume indicator

Evolution, development and learning using self-modifying cartesian genetic programming

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GP-rush: using genetic programming to evolve solvers for the rush hour puzzle

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Hybrid evolutionary algorithms for large scale continuous problems

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Improving Markov chain classification using string transformations and evolutionary search

Multi material topological optimization of structures and mechanisms

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SRaDE: an adaptive differential evolution based on stochastic ranking

New insights into the OCST problem: integrating node degrees and their location in the graph

Evolutionary multi-objective quantum control experiments with the covariance matrix adaptation

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Optimization of the trading rule in foreign exchange using genetic algorithm

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A polynomial time computation of the exact correlation structure of k-satisfiability landscapes

Using a distance metric to guide PSO algorithms for many-objective optimization

Overlapped community detection in complex networks

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Geometrical insights into the dendritic cell algorithm

Evolutionary optimization of multistage interconnection networks performance

Cooperative and decomposable approaches on royal road functions: overcoming the random mutation hill-climber

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Pareto cooperative coevolutionary genetic algorithm using reference sharing collaboration

Evolution of team composition in multi-agent systems

New entropy model for extraction of structural information from XCS population

A highly configurable test system for evolutionary black-box testing of embedded systems

Limitations of the fitness-proportional negative slope coefficient as a difficulty measure

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IMAD: in-execution malware analysis and detection

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uQFCS: QFCS with unfixed fuzzy sets in continuous multi-step environments with continuous vector actions

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Graph structured program evolution with automatically defined nodes

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Genetic programming methodology that synthesize vegetation indices for the estimation of soil cover

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Using crossover based similarity measure to improve genetic programming generalization ability

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Soft memory for stock market analysis using linear and developmental genetic programming

Behavioural GP diversity for adaptive stock selection

Problem decomposition using indirect reciprocity in evolved populations

Content Provider	ACM Digital Library
Author	Ofria, Charles Goings, Sherri Clune, Jeff Goldsby, Heather J.
Abstract	Evolutionary problem decomposition techniques divide a complex problem into simpler subproblems, evolve individuals to produce subcomponents that solve the subproblems, and then assemble the subcomponents to produce an overall solution. Ideally, these techniques would automatically decompose the problem and dynamically assemble the subcomponents to form the solution. However, although significant progress in automated problem decomposition has been made, most techniques explicitly assemble the complete solution as part of the fitness function. In this paper, we propose a digital-evolution technique that lays the groundwork for enabling individuals within the population to dynamically decompose a problem and assemble a solution. Specifically, our approach evolves specialists that produce some subcomponents of a problem, cooperate with others to receive different subcomponents, and then assemble the subcomponents to produce an overall solution. We first establish that this technique is able to evolve specialists that cooperate. We then demonstrate that it is more effective to use a generalist strategy, wherein organisms solve the entire problem themselves, on simple problems, but that a specialist strategy is better on complex problems. Finally, we show that our technique automatically selects a generalist or specialist strategy based on the complexity of the problem.
Starting Page	105
Ending Page	112
Page Count	8
File Format	PDF
ISBN	9781605583259
DOI	10.1145/1569901.1569917
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-07-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Binary string cover problem Evolution of cooperation Digital evolution Altruism
Content Type	Text
Resource Type	Article

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