- author = "Jason Cooper",
- title = "Improving performance of genetic algorithms by using novel fitness functions",
- school = "Loughborough University",
- year = "2006",
- address = "UK",
- month = "20 " # jan,
- keywords = "genetic algorithms, genetic programming, Fitness functions, Efficiency, Intelligent fitness functions, Partial fitness functions, Deceptive Trap Function, One Max Problem, GA Hard Problem",
- timestamp = "Tue, 05 Apr 2022 10:59:11 +0200",
-
biburl = "
https://dblp.org/rec/phd/ethos/Cooper06.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
-
URL = "https://dspace.lboro.ac.uk/2134/2271",
-
URL = "https://hdl.handle.net/2134/2271",
-
URL = "https://ethos.bl.uk/concern/thesis_or_dissertations/429223",
-
URL = "https://repository.lboro.ac.uk/articles/thesis/Improving_performance_of_genetic_algorithms_by_using_novel_fitness_functions/9406844?file=17023985",
-
URL = "https://repository.lboro.ac.uk/ndownloader/files/17023985/1",
- size = "281 pages",
-
abstract = "This thesis introduces Intelligent Fitness Functions
and Partial Fitness Functions both of which can improve
the performance of a genetic algorithm which is limited
to a fixed run time.
An Intelligent Fitness Function is defined as a fitness function with a memory. The memory is used to store information about individuals so that duplicate individuals do not need to have their fitness tested. Different types of memory (long and short term) and different storage strategies (fitness based, time base and frequency based) have been tested. The results show that an intelligent fitness function, with a time based long term memory improves the efficiency of a genetic algorithm the most.
A Partial Fitness Function is defined as a fitness function that only partially tests the fitness of an individual at each generation. Thus only promising individuals get fully tested. Using a partial fitness function gives the genetic algorithm more evolutionary steps in the same length of time as a genetic algorithm using a normal fitness function. The results show that a genetic algorithm using a partial fitness function can achieve higher fitness levels than a genetic algorithm using a normal fitness function.
Finally a genetic algorithm designed to solve a substitution cipher is compared to one equipped with an intelligent fitness function and another equipped with a partial fitness function. The genetic algorithm with the intelligent fitness function and the genetic algorithm with the partial fitness function both show a significant improvement over the genetic algorithm with a conventional fitness function.",
-
notes = "Some mention of GP. Nortel Networks EThOS ID:
uk.bl.ethos.429223
Supervisor: Chris Hinde",
