Dr Lee Christie's Outputs (25)

Generating easy and hard problems using the proximate optimality principle. [Dataset] (2015)
Data
MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2015. Generating easy and hard problems using the proximate optimality principle. [Dataset]

These data were gathered to investigate the hypothesis that coherent functions will be easy and anti-coherent functions will be hard for a hillclimber. We generated 10 coherent functions for each length on bit-strings of length 6-100 and the same num... Read More about Generating easy and hard problems using the proximate optimality principle. [Dataset].

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..

Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings. (2014)
Presentation / Conference Contribution
CHRISTIE, L.A., MCCALL, J.A.W. and LONIE, D.P. 2014. Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings. In Igel, C. (ed.) Proceedings of the 2014 Genetic and evolutionary computation conference (GECCO 2014): a recombination of the 23rd International conference on genetic algorithms (ICGA-2014), and the 19th Annual genetic programming conference (GP-2014), 12-16 July 2014, Vancouver, Canada. New York: ACM [online], pages 333-340. Available from: https://doi.org/10.1145/2576768.2598240

Problem structure, or linkage, refers to the interaction between variables in a black-box fitness function. Discovering structure is a feature of a range of algorithms, including estimation of distribution algorithms (EDAs) and perturbation methods (... Read More about Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings..

Partial structure learning by subset Walsh transform. (2013)
Presentation / Conference Contribution
CHRISTIE, L.A., LONIE, D.P. and MCCALL, J.A.W. 2013. Partial structure learning by subset Walsh transform. In Jin, Y. and Thomas, S.A. (eds.) Proceedings of the 13th UK workshop on computational intelligence (UKCI 2013), 9-11 September 2013, Guildford, UK. New York: IEEE [online], article number 6651297, pages 128-135. Available from: https://doi.org/10.1109/UKCI.2013.6651297

Estimation of distribution algorithms (EDAs) use structure learning to build a statistical model of good solutions discovered so far, in an effort to discover better solutions. The non-zero coefficients of the Walsh transform produce a hypergraph rep... Read More about Partial structure learning by subset Walsh transform..