This article is within the scope of WikiProject Robotics, a collaborative effort to improve the coverage of
Robotics on Wikipedia. If you would like to participate, please visit the project page, where you can join
the discussion and see a list of open tasks.RoboticsWikipedia:WikiProject RoboticsTemplate:WikiProject RoboticsRobotics articles
Problem solving with
evolutionary computation is realized with a cost function.[7] If cost functions are applied to swarm optimization they are called a fitness function. Strategies like reinforcement learning[10] and NEAT neuroevolution[3] are creating a
fitness landscape which describes the reproductive success of cellular automata.[1][5]
A normal fitness function fits to a problem[9], while an effective fitness function is an assumption if the objective was reached.[8] The difference is important for designing fitness function with algorithm like
novelty search in which the objective of the agents is unkown.[2] [6]
Notes
effective fitness is located in evolutionary computation [1]
Fitness = reproductive success [1]
Fitness landscapes is a set of fitness values connected configuration space [1]
is effective fitness a measurement for the entire fitness landscape? [1]
expected fitness function = Effective Fitness Function [8]
fitness function must fit to a problem [9]
Reinforcement Learning can search for the best (=effective) fitness function [10]
is "effective fitness function" = "cost function"?
additional notes
open:[4]
Literature
[1] Stadler, Peter F., and Christopher R. Stephens. "Landscapes and effective fitness." Comments® on Theoretical Biology 8.4-5 (2003): 389-431.
[2] Lehman, Joel, and Kenneth O. Stanley. "Abandoning objectives: Evolution through the search for novelty alone." Evolutionary computation 19.2 (2011): 189-223.
[3] Divband Soorati, Mohammad, and Heiko Hamann. "The effect of fitness function design on performance in evolutionary robotics: The influence of a priori knowledge." Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. 2015.
[4] Stephens, Christopher R. "Effect of mutation and recombination on the genotype-phenotype map." arXiv preprint nlin/0006051 (2000).
[6] Woolley, Brian G., and Kenneth O. Stanley. "Exploring promising stepping stones by combining novelty search with interactive evolution." arXiv preprint arXiv:1207.6682 (2012).
[7] Schaffer, J. David, Heike Sichtig, and Craig Laramee. "A series of failed and partially successful fitness functions for evolving spiking neural networks." Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers. 2009.
[8] Handa, Hisashi. "Fitness function for finding out robust solutions on time-varying functions." Proceedings of the 8th annual conference on Genetic and evolutionary computation. 2006.
[9] Fernandez, Aaron Carl T. "Creating a fitness function that is the right fit for the problem at hand." (2017).
[10] Afanasyeva, Arina, and Maxim Buzdalov. "Optimization with auxiliary criteria using evolutionary algorithms and reinforcement learning." Proceedings of 18th International Conference on Soft Computing MENDEL 2012. Vol. 2012. 2012.
This article is within the scope of WikiProject Robotics, a collaborative effort to improve the coverage of
Robotics on Wikipedia. If you would like to participate, please visit the project page, where you can join
the discussion and see a list of open tasks.RoboticsWikipedia:WikiProject RoboticsTemplate:WikiProject RoboticsRobotics articles
Problem solving with
evolutionary computation is realized with a cost function.[7] If cost functions are applied to swarm optimization they are called a fitness function. Strategies like reinforcement learning[10] and NEAT neuroevolution[3] are creating a
fitness landscape which describes the reproductive success of cellular automata.[1][5]
A normal fitness function fits to a problem[9], while an effective fitness function is an assumption if the objective was reached.[8] The difference is important for designing fitness function with algorithm like
novelty search in which the objective of the agents is unkown.[2] [6]
Notes
effective fitness is located in evolutionary computation [1]
Fitness = reproductive success [1]
Fitness landscapes is a set of fitness values connected configuration space [1]
is effective fitness a measurement for the entire fitness landscape? [1]
expected fitness function = Effective Fitness Function [8]
fitness function must fit to a problem [9]
Reinforcement Learning can search for the best (=effective) fitness function [10]
is "effective fitness function" = "cost function"?
additional notes
open:[4]
Literature
[1] Stadler, Peter F., and Christopher R. Stephens. "Landscapes and effective fitness." Comments® on Theoretical Biology 8.4-5 (2003): 389-431.
[2] Lehman, Joel, and Kenneth O. Stanley. "Abandoning objectives: Evolution through the search for novelty alone." Evolutionary computation 19.2 (2011): 189-223.
[3] Divband Soorati, Mohammad, and Heiko Hamann. "The effect of fitness function design on performance in evolutionary robotics: The influence of a priori knowledge." Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. 2015.
[4] Stephens, Christopher R. "Effect of mutation and recombination on the genotype-phenotype map." arXiv preprint nlin/0006051 (2000).
[6] Woolley, Brian G., and Kenneth O. Stanley. "Exploring promising stepping stones by combining novelty search with interactive evolution." arXiv preprint arXiv:1207.6682 (2012).
[7] Schaffer, J. David, Heike Sichtig, and Craig Laramee. "A series of failed and partially successful fitness functions for evolving spiking neural networks." Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers. 2009.
[8] Handa, Hisashi. "Fitness function for finding out robust solutions on time-varying functions." Proceedings of the 8th annual conference on Genetic and evolutionary computation. 2006.
[9] Fernandez, Aaron Carl T. "Creating a fitness function that is the right fit for the problem at hand." (2017).
[10] Afanasyeva, Arina, and Maxim Buzdalov. "Optimization with auxiliary criteria using evolutionary algorithms and reinforcement learning." Proceedings of 18th International Conference on Soft Computing MENDEL 2012. Vol. 2012. 2012.