 The evolutionary computation bibliography with notes 








 by Yury Tsoy (look for updates at: http://qai.narod.ru/Papers/ecb.zip, email me to: qai@mail.ru) 


















 Last updated:  ^ 







 Titles  ^ 







 Personalities  107 







 Research Groups  65 

















 All papers are ordered (firstly) by Author(s) and (secondly) by Year 








 Some comments and notes are given in Russian and a work for their translation into English is still in progress. If you need a translation which is missing please send me a request. 


















 Other EC bibliographies: 








 Online EA bibliography at Stuttgart: http://www.fmi.unistuttgart.de/fk/evolalg/eabib.html 








 Online bibliography of GECCO and some other conferences (by W. Langdon): http://liinwww.ira.uka.de/bibliography/Ai/gecco.html 


















 Classification notation: 








 EA  Evolutionary Algorithms 







 GA  Genetic Algorithms 







 EP  Evolutionary Programming 







 GP  Genetic Programming 







 ES  Evolutionary Strategies 







 ALife  Artificial Life and BioCybernetics 







 RC  Real Coding of parameters 







 NE  NeuroEvolution 







 Bio  Biology (evolution, genetics, etc.) 







 IE  Interactive evolution (aesthetic selection) 







 WL  Wavelet 







 CA  Cellular Automata 







 PSO  Particles Swarm Optimization 







 DE  Differential Evolution 







 SO  SelfOrganization 







 survey  a survey ;) 

















#  Authors  Title  Publisher, Journal  Year  Abstract  Comments & Notes  Class  Filename 

 Aggarwal V.  Solving transcendental equations using Genetic Algorithms   
 GA for multiObjective optimization problems  GA 


 Altenberg L.  The Evolution of Evolvability in Genetic Programming.  Advances in Genetic Programming.  MIT Press, 1994.  1994   Upward mobility selection: recombinant offspring is only placed in populaiton when it is fitter then its parents  GA 


 Altenberg L.  The schema theorem and Price’s theorem.  Foundations of Genetic Algorithms, 3.  1995.  P.23–50.  1995   Use of Geiringer's theorem to predict evolution of fitness distribution in result of recombination. Schema theorem is conbined with marginal recombination distribution and Price's theorem.  GA  altenberg_1995.pdf 

 Altenberg L.  Open Problems in the Spectral Analysis of Evolutionary Dynamics  Chapter 4 in Frontiers of Evolutionary Computation, ed. Anil Menon, Genetic Algorithms And Evolutionary Computation Series. Vol. 11.  Kluwer Academic Publishers, Boston, MA, 2004.  P.73102.  2004  For broad classes of selection and genetic operators, the dynamics of evolution can be completely characterized by the spectra of the operators that define the dynamics, in both infinite and finite populations. These classes include generalized mutation, frequencyindependent selection, uniparental inheritance. Several open questions exist regarding these spectra: (1) For a given fitness function, what genetic operators and operator intensities are optimal for finding the fittest genotype? The concept of rapid first hitting time, an analog of Sinclair’s ”rapidly mixing” Markov chains, is examined. (2) What is the relationship between the spectra of deterministic infinite population models, and the spectra of the Markov processes derived from them in the case of finite populations? (3) Karlin proved a fundamental relationship between selection, rates of transformation under genetic operators, and the consequent asymptotic mean fitness of the population. Developed to analyze the stability of polymorphisms in subdivided populations, the theorem has been applied to unify the reduction principle for selfadaptation, and has other applications as well. Many other problems could be solved if it were generalized to account for the interaction of different genetic operators. Can Karlin’s theorem on operator intensity be extended to account for mixed genetic operators?  A lot of maths :)  GA  altenberg_2004.pdf 

 AlvarezBenitez J.E., Everson R.M., Fieldsen J.E.  A MOPSO algorithm based exclusively on pareto dominance concepts  In C. CoelloCoello et al. (eds.): Evolutionary MultiCriterion Optimization, vol. 3410, pp. 459473. Springer, 2005  2005   MOPSO for multiobjective optimization  PSO 


 Ando S., Ishizuka M., Iba H.  Evolving analog circuits by variable length chromosomes  in A. Ghosh and S. Tsutsui (Eds): Advances in evolutionary computing. New York: Springer, 2003, pp. 643–662.  2003   The direct encoding has been applied to the synthesis of electronic circuits  EC 


 Angeline P.J.  Adaptive and selfadaptive evolutionary computations  M. Palaniswami, Y. Attikiouzel, R. Marks, D.B. Fogel, T. Fukuda (Eds.), Computational Intelligence: A Dynamic Systems Perspective.  IEEE Press, New York, 1995.  P.152–163.  1995   Adaptation of GAs parameters (a survey)  EA, survey 


 Angeline P.J., Saunders G.M., Pollack J.B.  An Evolutionary Algorithm that Constructs Recurrent Neural Networks  IEEE Transactions on Neural Networks.  1994.  No.5(1)  P.5465.  1994  Standard methods for inducing both the structure and weight values of recurrent neural networks fit an assumed class of architectures to every task. This simplification is necessary because the interactions between network structure and function are not well understood. Evolutionary computation, which includes genetic algorithms and evolutionary programming, is a populationbased search method that has shown promise in such complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. This algorithm’s empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods.  GNARL  GeNeralized Acquisition of Recurrent Links algorithm is described. Max number of hidden nodes is defined by user and those nodes are always present in the ANNs structures, even though they are not connected with other neurons just to provide "resource" for mutation. Use of neural offsets is also defined by user. An opinion that GA is not suited for searching the weight space and one should use evolutionary programming techniques since they only use mutation.  NE, EP  gnarlytr.pdf 

 Arabas J., Michalewicz Z., Mulawka J.  GAVAPS—a genetic algorithm with varying population size  Proceedings of the First IEEE International Conference on Evolutionary Computation.  IEEE Press, New York, 1994  P.73–78.  1994   Population sizing using individuals lifetime parameter, which is defined according to the current evolutinary search state. Lifetime depends on the fitness of the "newborn"  GA 


 Astor J.C., Adami C.  A Developmental Model for the Evolution of Artificial Neural Networks  Artificial Life.  №6  P.189–218  2000  We present a model of decentralized growth and development for artificial neural networks (ANNs), inspired by developmental biology and the physiology of nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates. The chemicals and substrates, in turn, are modeled by a simple arti.cial chemistry. While the system is designed to allow for the evolution of complex networks, we demonstrate the power of the arti.cial chemistry by analyzing engineered (handwritten) genomes that lead to the growth of simple networks with behaviors known from physiology. To evolve more complex structures, a Javabased, platformindependent, asynchronous, distributed genetic algorithm (GA) has been implemented that allows users to participate in evolutionary experiments via the World Wide Web.  Keywords: artificial neural network, (distributed) genetic algorithms, neurogenesis, gene expression, artificial chemistry, artificial life, Java. The evolution of single neural nodes is considered on the hexagonal plane with use of artificial chemistry principles. Examples of evolutionary developmental systems for the case of artificial neural networks  ALife  astor_Alife2000.pdf  **** 
 Aytug H., Koehler G.J.  Stopping criteria for finite length genetic algorithms  ORSA Journal on Computing.  1996.  No.8(2).  P.183191.  1996   Upper bound for the generations number that necessary to converge to the optimal strings population (with respect to mutation probability)  GA 

