All Outputs (2)

Generating easy and hard problems using the proximate optimality principle. (2015)
Presentation / Conference Contribution
MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2015. Generating easy and hard problems using the proximate optimality principle. In Silva, S. (ed.) Proceedings of the companion publication of the 2015 annual conference on genetic and evolutionary computation (GECCO Companion '15), 11-15 July 2015, Madrid, Spain. New York: ACM [online], pages 767-768. Available from: https://doi.org/10.1145/2739482.2764890

We present an approach to generating problems of variable difficulty based on the well-known Proximate Optimality Principle (POP), often paraphrased as similar solutions have similar fitness. We explore definitions of this concept in terms of metrics... Read More about Generating easy and hard problems using the proximate optimality principle..

Structural coherence of problem and algorithm: an analysis for EDAs on all 2-bit and 3-bit problems. (2015)
Presentation / Conference Contribution
BROWNLEE, A.E.I., MCCALL, J.A.W. and CHRISTIE, L.A. 2015. Structural coherence of problem and algorithm: an analysis for EDAs on all 2-bit and 3-bit problems. In Proceedings of the 2015 IEEE congress on evolutionary computation (CEC 2015), 25-28 May 2015, Sendai, Japan. Piscataway, NJ: IEEE [online], pages 2066-2073. Available from: https://doi.org/10.1109/CEC.2015.7257139

Metaheuristics assume some kind of coherence between decision and objective spaces. Estimation of Distribution algorithms approach this by constructing an explicit probabilistic model of high fitness solutions, the structure of which is intended to r... Read More about Structural coherence of problem and algorithm: an analysis for EDAs on all 2-bit and 3-bit problems..