Lecture Notes in Computer Science - Evolutionary Programming VII
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Published By Springer Berlin Heidelberg

9783540648918, 9783540685159

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Culturing evolution strategies to support the exploration of novel environments by an intelligent robotic agent

Lecture Notes in Computer Science - Evolutionary Programming VII ◽

10.1007/bfb0040775 ◽

1998 ◽

pp. 219-228

Author(s):

Chan-Jin Chung ◽

Robert G. Reynolds

Keyword(s):

Evolution Strategies ◽

Novel Environments ◽

Robotic Agent

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Fuzzy partition and input selection by genetic algorithms for designing fuzzy rule-based classification systems

Lecture Notes in Computer Science - Evolutionary Programming VII ◽

10.1007/bfb0040793 ◽

1998 ◽

pp. 407-416 ◽

Cited By ~ 4

Author(s):

Tadahiko Murata ◽

Hisao Ishibuchi ◽

Tomoharu Nakashima ◽

Mitsuo Gen

Keyword(s):

Genetic Algorithms ◽

Fuzzy Rule ◽

Classification Systems ◽

Rule Based ◽

Input Selection ◽

Fuzzy Partition

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An evolutionary algorithm for designing feedforward neural networks

Lecture Notes in Computer Science - Evolutionary Programming VII ◽

10.1007/bfb0040814 ◽

1998 ◽

pp. 629-635 ◽

Cited By ~ 1

Author(s):

Alexei N. Skourikhine

Keyword(s):

Neural Networks ◽

Evolutionary Algorithm ◽

Feedforward Neural Networks

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On making problems evolutionarily friendly part 2: Evolving the most convenient coordinate systems within which to pose (and solve) the given problem

Lecture Notes in Computer Science - Evolutionary Programming VII ◽

10.1007/bfb0040781 ◽

1998 ◽

pp. 281-290 ◽

Cited By ~ 3

Author(s):

A. V. Sebald ◽

K. Chellapilla

Keyword(s):

Coordinate Systems ◽

The Given

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Automated rule extraction for engine health monitoring

Lecture Notes in Computer Science - Evolutionary Programming VII ◽

10.1007/bfb0040823 ◽

1998 ◽

pp. 723-734 ◽

Cited By ~ 1

Author(s):

T. W. Brotherton ◽

G. Chadderdon

Keyword(s):

Health Monitoring ◽

Rule Extraction

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Parameter selection in particle swarm optimization

Lecture Notes in Computer Science - Evolutionary Programming VII ◽

10.1007/bfb0040810 ◽

1998 ◽

pp. 591-600 ◽

Cited By ~ 1348

Author(s):

Yuhui Shi ◽

Russell C. Eberhart

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Parameter Selection ◽

Swarm Optimization

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Numeric mutation as an improvement to symbolic regression in genetic programming

Lecture Notes in Computer Science - Evolutionary Programming VII ◽

10.1007/bfb0040778 ◽

1998 ◽

pp. 251-260 ◽

Cited By ~ 9

Author(s):

Thomas Fernandez ◽

Matthew Evett

Keyword(s):

Genetic Programming ◽

Symbolic Regression

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GA-Optimal fitness functions

Lecture Notes in Computer Science - Evolutionary Programming VII ◽

10.1007/bfb0040763 ◽

1998 ◽

pp. 95-102 ◽

Cited By ~ 1

Author(s):

Joseph L. Breeden

Keyword(s):

Fitness Functions

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An evolutionary self-learning methodology: Some preliminary results from a case study

Lecture Notes in Computer Science - Evolutionary Programming VII ◽

10.1007/bfb0040791 ◽

1998 ◽

pp. 387-396

Author(s):

Sharad Thacore

Keyword(s):

Preliminary Results ◽

Self Learning

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Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences

Lecture Notes in Computer Science - Evolutionary Programming VII ◽

10.1007/bfb0040811 ◽

1998 ◽

pp. 601-610 ◽

Cited By ~ 496

Author(s):

Peter J. Angeline

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Evolutionary Optimization ◽

Swarm Optimization ◽

Performance Differences ◽

And Performance ◽

Versus Particle

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Lecture Notes in Computer Science - Evolutionary Programming VIILatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

Culturing evolution strategies to support the exploration of novel environments by an intelligent robotic agent

Fuzzy partition and input selection by genetic algorithms for designing fuzzy rule-based classification systems

An evolutionary algorithm for designing feedforward neural networks

On making problems evolutionarily friendly part 2: Evolving the most convenient coordinate systems within which to pose (and solve) the given problem

Automated rule extraction for engine health monitoring

Parameter selection in particle swarm optimization

Numeric mutation as an improvement to symbolic regression in genetic programming

GA-Optimal fitness functions

An evolutionary self-learning methodology: Some preliminary results from a case study

Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences

Lecture Notes in Computer Science - Evolutionary Programming VII
Latest Publications