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GECCO
2010
Springer

Investigating EA solutions for approximate KKT conditions in smooth problems

9 years 12 days ago
Investigating EA solutions for approximate KKT conditions in smooth problems
Evolutionary algorithms (EAs) are increasingly being applied to solve real-parameter optimization problems due to their flexibility in handling complexities such as non-convexity, non-differentiability, multi-modality and noise in problems. However, an EA's solution is never guaranteed to be optimal in generic problems, even for smooth problems, and importantly EAs still lack a theoretically motivated termination criterion for stopping an EA run only when a near-optimal point is found. We address both these issues in this paper by integrating the Karush-Kuhn-Tucker (KKT) optimality conditions that involve first-order derivatives of objective and constraint functions with an EA. For this purpose, we define a KKT-proximity measure by relaxing the complimentary slackness condition associated with the KKT conditions. Results on a number of standard constrained test problems indicate that in spite of not using any gradient information and any theoretical optimality conditions, an EA&#...
Rupesh Tulshyan, Ramnik Arora, Kalyanmoy Deb, Joyd
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where GECCO
Authors Rupesh Tulshyan, Ramnik Arora, Kalyanmoy Deb, Joydeep Dutta
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