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» On the Brittleness of Evolutionary Algorithms
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DATE
2008
IEEE
102views Hardware» more  DATE 2008»
16 years 19 days ago
A New Approach for Combining Yield and Performance in Behavioural Models for Analogue Integrated Circuits
A new algorithm is presented that combines performance and variation objectives in a behavioural model for a given analogue circuit topology and process. The tradeoffs between per...
Sawal Ali, Reuben Wilcock, Peter R. Wilson, Andrew...
GECCO
2007
Springer
209views Optimization» more  GECCO 2007»
16 years 9 days ago
An online implementable differential evolution tuned optimal guidance law
This paper proposes a novel application of differential evolution to solve a difficult dynamic optimisation or optimal control problem. The miss distance in a missile-target engag...
Raghunathan Thangavelu, S. Pradeep
GECCO
2007
Springer
137views Optimization» more  GECCO 2007»
16 years 9 days ago
Robust multi-cellular developmental design
This paper introduces a continuous model for Multi-cellular Developmental Design. The cells are fixed on a 2D grid and exchange ”chemicals” with their neighbors during the gr...
Alexandre Devert, Nicolas Bredeche, Marc Schoenaue...
GECCO
2007
Springer
163views Optimization» more  GECCO 2007»
16 years 9 days ago
Choice and development
The process of development creates a phenotype from one or more genotypes of an individual through interaction with an environment. The opportunity for development to choose a phe...
Arthur M. Farley
IWCLS
2007
Springer
16 years 9 days ago
On Lookahead and Latent Learning in Simple LCS
Learning Classifier Systems use evolutionary algorithms to facilitate rule- discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most c...
Larry Bull