All Outputs (31)

Memetic algorithms: The polynomial local search complexity theory perspective (2008)
Journal Article
Krasnogor, N., & Smith, J. (2008). Memetic algorithms: The polynomial local search complexity theory perspective. Journal of Mathematical Modelling and Algorithms, 7(1), 3-24. https://doi.org/10.1007/s10852-007-9070-9

In previous work (Krasnogor, http://www.cs.nott.ac.uk/~nxk/papers.html . In: Studies on the Theory and Design Space of Memetic Algorithms. Ph.D. thesis, University of the West of England, Bristol, UK, 2002; Krasnogor and Smith, IEEE Trans Evol Algori... Read More about Memetic algorithms: The polynomial local search complexity theory perspective.

Parameter control in evolutionary algorithms (2007)
Journal Article
Eiben, A., Michalewicz, Z., Schoenauer, M., & Smith, J. (2007). Parameter control in evolutionary algorithms. Studies in Computational Intelligence, 54, 19-46. https://doi.org/10.1007/978-3-540-69432-8_2

The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed... Read More about Parameter control in evolutionary algorithms.

On replacement strategies in steady state evolutionary algorithms (2007)
Journal Article
Smith, J. (2007). On replacement strategies in steady state evolutionary algorithms. Evolutionary Computation, 15(1), 29-59. https://doi.org/10.1162/evco.2007.15.1.29

Steady State models of Evolutionary Algorithms are widely used, yet surprisingly little attention has been paid to the effects arising from different replacement strategies. This paper explores the use of mathematical models to characterise the selec... Read More about On replacement strategies in steady state evolutionary algorithms.

A tutorial for competent memetic algorithms: Model, taxonomy and design issues (2005)
Journal Article
Krasnogor, N., & Smith, J. (2005). A tutorial for competent memetic algorithms: Model, taxonomy and design issues. IEEE Transactions on Evolutionary Computation, 9(5), 474-488. https://doi.org/10.1109/TEVC.2005.850260

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learni... Read More about A tutorial for competent memetic algorithms: Model, taxonomy and design issues.

Book review: Introduction to evolutionary computing (2004)
Journal Article
Eiben, A. E., & Smith, J. (2004). Book review: Introduction to evolutionary computing. Evolutionary Computation, 12(2), 269-271. https://doi.org/10.1162/evco.2004.12.2.269

Introduction to evolutionary computing (2004)
Journal Article
Eiben, A. E., & Smith, J. (2004). Introduction to evolutionary computing. Computing Reviews, 45(7), 415

Authors' answer to the book review of Introduction to Evolutionary Computing Published in Issue 12: 2 (2004)
Journal Article
Eiben, A., & Smith, J. (2004). Authors' answer to the book review of Introduction to Evolutionary Computing Published in Issue 12: 2. Evolutionary Computation, 12(3), 395-395. https://doi.org/10.1162/1063656041775027

Editorial introduction to special issue on memetic algorithms (2004)
Journal Article
Smith, J., Krasnogor, N., & Hart, W. (2004). Editorial introduction to special issue on memetic algorithms. Evolutionary Computation, 12(3), 3-4. https://doi.org/10.1162/1063656041775009

On appropriate adaptation levels for the learning of gene linkage (2002)
Journal Article
Smith, J. (2002). On appropriate adaptation levels for the learning of gene linkage. Genetic Programming and Evolvable Machines, 3(2), 129-155. https://doi.org/10.1023/A%3A1015579825262

Replacement strategies in steady state genetic algorithms: Dynamic environments (1999)
Journal Article
Smith, J., & Vavak, F. (1999). Replacement strategies in steady state genetic algorithms: Dynamic environments. Journal of Computing and Information Technology, 7(1), 49-60

Operator and parameter adaptation in genetic algorithms (1997)
Journal Article
Smith, J., & Fogarty, T. (1997). Operator and parameter adaptation in genetic algorithms. Soft Computing, 1(2), 81-87. https://doi.org/10.1007/s005000050009

Genetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor of “Natural Selection”. These algorithms maintain a finite memory of individual points on the search landscape known as the... Read More about Operator and parameter adaptation in genetic algorithms.