In natural evolution and artificial evolution (e.g. artificial life and evolutionary computation) the fitness (or performance or objective measure) of a schema is rescaled to give its effective fitness which takes into account crossover and mutation.
Effective fitness is used in Evolutionary Computation to understand population dynamics.[1] While a biological fitness function only looks at reproductive success, an effective fitness function tries to encompass things that are needed to be fulfilled for survival on population level.[2] In homogeneous populations, reproductive fitness and effective fitness are equal.[1] When a population moves away from homogeneity a higher effective fitness is reached for the recessive genotype. This advantage will decrease while the population moves toward an equilibrium.[1] The deviation from this equilibrium displays how close the population is to achieving a steady state.[1] When this equilibrium is reached, the maximum effective fitness of the population is achieved.[3]
Problem solving with evolutionary computation is realized with a cost function.[4] If cost functions are applied to swarm optimization they are called a fitness function. Strategies like reinforcement learning[5] and NEAT neuroevolution[6] are creating a fitness landscape which describes the reproductive success of cellular automata.[7][8]
The effective fitness function models the number of fit offspring[1] and is used in calculations that include evolutionary processes, such as mutation and crossover, important on the population level.[9]
The effective fitness model is superior to its predecessor, the standard reproductive fitness model. It advances in the qualitatively and quantitatively understanding of evolutionary concepts like bloat, self-adaptation, and evolutionary robustness.[3] While reproductive fitness only looks at pure selection, effective fitness describes the flow of a population and natural selection by taking genetic operators into account.[1][3]
A normal fitness function fits to a problem,[10] while an effective fitness function is an assumption if the objective was reached.[11] The difference is important for designing fitness functions with algorithms like novelty search in which the objective of the agents is unknown.[12][13] In the case of bacteria effective fitness could include production of toxins and rate of mutation of different plasmids, which are mostly stochastically determined[14]
^ abcdefStephens CR (1999). ""Effective" fitness landscapes for evolutionary systems". Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406). pp. 703–714. arXiv:nlin/0006050. doi:10.1109/CEC.1999.782002. ISBN 0-7803-5536-9. S2CID 10062119.
^von Bronk B, Schaffer SA, Götz A, Opitz M (May 2017). Balaban N (ed.). "Effects of stochasticity and division of labor in toxin production on two-strain bacterial competition in Escherichia coli". PLOS Biology. 15 (5): e2001457. doi:10.1371/journal.pbio.2001457. PMC 5411026. PMID 28459803.
^ abcStephens CR, Vargas JM (2000). "Effective Fitness as an Alternative Paradigm for Evolutionary Computation I: General Formalism". Genetic Programming and Evolvable Machines. 1 (4): 363–378. doi:10.1023/A:1010017207202. S2CID 1511583.
^Schaffer JD, Sichtig HM, Laramee C (2009). 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 - GECCO 09. ACM Press. doi:10.1145/1570256.1570378.
^Afanasyeva A, Buzdalov M (2012). Optimization with auxiliary criteria using evolutionary algorithms and reinforcement learning. Proceedings of 18th International Conference on Soft Computing MENDEL 2012. Vol. 2012. pp. 58–63.
^Divband Soorati M, Hamann H (2015). The Effect of Fitness Function Design on Performance in Evolutionary Robotics. Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO 15. ACM Press. doi:10.1145/2739480.2754676.
^Stadler PF, Stephens CR (2003). "Landscapes and Effective Fitness". Comments on Theoretical Biology. 8 (4–5). Informa UK Limited: 389–431. doi:10.1080/08948550302439.
^Bagnoli F (1998). "Cellular automata". arXiv:cond-mat/9810012.
^Henry A, Hemery M, François P (June 2018). "φ-evo: A program to evolve phenotypic models of biological networks". PLOS Computational Biology. 14 (6): e1006244. Bibcode:2018PLSCB..14E6244H. doi:10.1371/journal.pcbi.1006244. PMC 6013240. PMID 29889886.
^Fernandez AC (2017). "Creating a fitness function that is the right fit for the problem at hand". {{cite journal}}: Cite journal requires |journal= (help)
^Handa H (2006). Fitness function for finding out robust solutions on time-varying functions. Proceedings of the 8th annual conference on Genetic and evolutionary computation GECCO 06. ACM Press. CiteSeerX 10.1.1.421.930. doi:10.1145/1143997.1144186.
^Lehman J, Stanley KO (2011). "Abandoning objectives: evolution through the search for novelty alone". Evolutionary Computation. 19 (2). MIT Press - Journals: 189–223. doi:10.1162/evco_a_00025. PMID 20868264. S2CID 12129661.
^Woolley BF, Stanley KO (2012). "Exploring promising stepping stones by combining novelty search with interactive evolution". arXiv:1207.6682 [cs.NE].
^Lehman J, Stanley KO (2010-09-24). "Abandoning objectives: evolution through the search for novelty alone". Evolutionary Computation. 19 (2): 189–223. doi:10.1162/EVCO_a_00025. PMID 20868264. S2CID 12129661.
evolutionary computation) the fitness (or performance or objective measure) of a schema is rescaled to give its effectivefitness which takes into account...
drives a selection for producing children at younger ages, the advent of effective contraception, higher education, and changing social norms have driven...
This can be more effective on dynamic problems.[citation needed] GAs cannot effectively solve problems in which the only fitness measure is a binary...
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Beginners: A Step-by-Step Guide to the World's Easiest-to-Learn, Most-EffectiveFitness and Fighting Program. Ulysses Press. ISBN 978-1569755372. Retrieved...
formulae, and then keeping whichever candidate solution has the best score or fitness on the optimization problem at hand. In this way, the optimization problem...
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evolution strategies is deterministic and only based on the fitness rankings, not on the actual fitness values. The resulting algorithm is therefore invariant...
similar to ski poles. Nordic walking (originally Finnish sauvakävely) is fitness walking with specially designed poles. While trekkers, backpackers, and...
unchanged and uses only the improved fitness. This pseudo code leaves open which steps are based on the fitness of the individuals and which are not....
result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm. Evolutionary computation techniques...
complexity nonetheless. Besides, through the utilization of a scale-adaptive fitness evaluation mechanism, PSO can efficiently address computationally expensive...
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program (its behaviour) is judged against some target behaviour, using a fitness function. However, LGP is generally more efficient than tree genetic programming...
that physical activity interventions are effective for increasing cardiovascular fitness. Cardiovascular fitness is a measure of how well the heart, lungs...
the program. The fitness (or error) is computed in a standard manner. For instance, in the case of symbolic regression, the fitness is the sum of differences...
reproduction (crossover), replication and/or mutation according to a predefined fitness measure, usually proficiency at the desired task. The crossover operation...
individuals of the population. The best individuals can be selected using a fitness function that assesses the performance of each individual in population...
Cardiorespiratory fitness (CRF) refers to the ability of the circulatory and respiratory systems to supply oxygen to skeletal muscles during sustained...
factor. In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty...
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effect deleterious alleles will have on fitness. If the population is below the critical effective size fitness will decrease drastically, however if the...