L.J. Fogel, A.J. Owens, and M.J. Walsh. Artificial Intelligence through Simulated Evolution. Wiley, New York, 1966. Home/Search Document Not in Database Summary Related Articles Check |
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Evolutionary Multiobjective Optimization - Using Cultural Algorithm (Correct)
....the use of a cultural algorithm combined with evolutionary programming (Cultural Algorithm with Evolutionary Programming [1] or CAEP) The pseudocode of our approach is shown in Algorithm 1. Here, we can clearly see the similarities of our approach with traditional evolutionary programming [8], and we also include the steps where the belief space is incorporated. The problems that we are interested in solving have decision variables and B objective functions. The population consists of a set of individuals, each of which repre Influence Selection Performance Variation ....
Lawrence J. Fogel. Artificial Intelligence through Simulated Evolution. Forty Years of Evolutionary Programming. John Wiley & Sons, Inc., New York, 1999.
Evolutionary Computation: Comments on the History and.. - Bäck, Hammel, Schwefel (1997) (Correct)
....[15] 16] Since this is true for all three of the main stream algorithms presented in this paper we will discuss their capabilities and performance mainly as optimization strategies. Evolutionary programming, introduced by Fogel [9] 38] and extended in Burgin [39] 40] Atmar [41] Fogel [42], 43] 44] and others, was originally offered as an attempt to create artificial intelligence. The approach was to evolve finite state machines (FSM) to predict events on the basis of former observations. An FSM is an abstract machine which transforms a sequence of input symbols into a sequence ....
.... strategies and evolutionary programming is directly based on real valued vectors when dealing with continuous parameter optimization problems of the general form f : M IR Both methods have originally been developed and are also used, however, for combinatorial optimization problems [43] [42], 55] Moreover, since many real world problems have complex search spaces which cannot be mapped canonically to one of the representations mentioned so far, lots of strategy variants, e.g. for integer [114] mixedinteger [115] structure optimization [116] 117] and others [82, chapter 10] ....
L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence through Simulated Evolution, Wiley, New York, 1966.
Amplification of Perspectives in the Use of - Evolutionary Computation Jovelino (Correct)
....forming the offspring. The selection of individuals that will form the next generation is made probabilistically, according to the respective values of fitness, thus allowing that a couple generate a larger or smaller number of descendants. 2.2. Evolutionary Programming EP Lawrence J. Fogel [8] proposed this method where a behavioral link between parents and offspring is emphasized, as opposed to the GA, where natural selection is directly emulated with the survival of the fittest. The application of this methodology field is specially suited to problems where the adaptive surface for ....
Fogel, L. J., A. J. O. Fogel, and M. J Walsh, Artificial Intelligence through simulated evolution, John Wiley, 1966.
Network Restoration Using Recurrent Neural Networks - Kumar, Venkataram (Correct)
....Dijkstra s deterministic shortest path algorithm [7] to find a (near )optimal route. Some of the techniques are (i) simulated annealing that uses Boltzman function [18] ii) neural networks based on Hopfiled model [2] 29] iii) genetic algorithms [5] iv) evolutionary algorithm [8] 9] 10][11], v) nearest neighbour [27] vi) branch and bound [12] and (vii) local search [16] Methods (i) iv) solve the problem by formulating it as some form of traveling sales person (TSP) problem [27] In designing the routing algorithm [25] an assumption is made that, the path with minimum hop ....
L. J. Fogel, A. J. Owens and M. J. Walsh, Artificial Intelligence through Simulated Evolution, John Wiley, New York, 1966.
AI Approaches to Network Management: Recent Advances and A.. - Kumar, Venkataram (Correct)
....In order to make the network management fault tolerant, more than one management center is assigned to each cluster of nodes in the partition. Gradient descent partition methods, converge to locally optimal partitions. In contrast, a stochastic search 10 method called evolutionary programming [37][38] 41] is employed in [42] to search for a globally optimal partition that minimizes the communication cost. 4.2 NM Functionalities Fault management module takes the symptoms (abnormal behavior) of the network and diagnoses to yield the faults that caused such a behavior. The information ....
L. J. Fogel, A. J. Owens and M. J. Walsh, Artificial Intelligence through Simulated Evolution, John Wiley, New York, 1966.
Enzyme Genetic Programming - Lones, Tyrrell (2001) (Correct)
....search for and optimise any structure which can be represented on a computer. Programs which implement artificial evolution are called evolutionary algorithms (EAs) There were three original forms of EA: genetic algorithms [14, 13] evolution strategies [36, 33, 5] and evolutionary programming [12]; and whilst contemporary algorithms now form a continuum of approaches, these three terms are still commonly used as a basis for classification. The activity of an EA revolves around a fundamental data structure called the population; a collection of individuals, each of which encodes a ....
L. Fogel, A. Owens, and M Walsh. Artificial intelligence through simulated evolution. Wiley, 1966.
Making Use of Population Information in Evolutionary Artificial.. - Yao, Liu (1998) (22 citations) (Correct)
....in the last generation to form an integrated system is expected to produce better results. This paper confirms that this is true by conducting a set of computational studies. III. AN EVOLUTIONARY DESIGN SYSTEM FOR ANNS EPNet EPNet is an automatic system based on evolutionary programming (EP) [19], 20] for designing feedforward ANN s [7] 9] The main structure of the system is shown in Fig. 1. EPNet does not use recombination operators in the simulated evolution in order to avoid the permutation (i.e. competing conventions) problem [21] 23] It relies on novel mutations and a ....
L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence Through Simulated Evolution. New York: Wiley, 1966.
A New Evolutionary System for Evolving Artificial Neural Networks - Yao, Liu (1996) (28 citations) (Correct)
....and pruning algorithms mentioned above. This paper describes a new evolutionary system, i.e. EPNet, for evolving feedforward ANN s. It combines the architectural evolution with the weight learning. The evolutionary algorithm used to evolve ANN s is based on Fogel s evolutionary programming (EP) [1] [3] It is argued in this paper that EP is a better candidate than genetic algorithms (GA s) for evolving ANN s. EP s emphasis on the behavioral link between parents and offspring can increase the efficiency of ANN s evolution. EPNet is different from previous work on evolving ANN s on a number ....
....at all. EPNet, on the other hand, can achieve very low error for a very compact ANN. V. CONCLUSION This paper describes a new EP based system, EPNet, for evolving feedforward ANN s. The idea behind EPNet is to put more emphasis on evolving ANN behaviors, rather than just its circuitry. EP [1], 2] 3] is better suited for evolving behaviors due to its emphasis on maintaining behavioral links between a parent and its offspring. EP also helps to avoid the permutation problem suffered by many EANN systems. A number of techniques have been adopted in EPNet to maintain a close behavioral ....
L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence Through Simulated Evolution. New York: Wiley, 1966.
Automated Synthesis of Analog Electrical Circuits by Means of.. - Koza, al. (1997) (24 citations) (Correct)
....connections and components in the topology. Again, annealing is used to perturb the topology by removing elements from a usersupplied superset. The possibility of applying evolutionary computation to design problems was recognized in the earliest pioneering work, including efforts in the 1960s [12, 13]. These methods have been extended to evolve the length and structure of recursive digital filters with infinite impulse response (IIR) as well as numerical coefficients [14; and others] Similar procedures have been offered within genetic algorithms (GAs) see [15] for background) using numerical ....
L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence through Simulated Evolution, New York: John Wiley, 1966.
Constrained Optimization Using an Evolutionary.. - Coello, Becerra (2002) (Correct)
....generation. 8. Update the belief space by accepting individuals using the acceptance function. 9. Go back to step 4 unless the available execution time is exhausted or an acceptable solution has been discovered. Most of the steps previously described are the same as in evolutionary programming [7]. The function accept( accepts those individuals that can contribute with their knowledge to the belief space. The function update( creates the new belief space with the beliefs of the accepted individuals. The idea is to add to the current knowledge the new knowledge acquired by the accepted ....
Lawrence J. Fogel. Artificial Intelligence through Simulated Evolution. Forty Years of Evolutionary Programming. John Wiley & Sons, Inc., New York,
Quantum-inspired Evolutionary Algorithm for a Class of.. - Han, Kim (2002) (Correct)
....although performance is affected by these heuristics. The three main stream methods of evolutionary computation which have been established over the past 45 years are genetic algorithms (GAs) developed by Fraser [1] Bremermann [2] and Holland [3] evolutionary programming (EP) developed by Fogel [4], and evolution strategies (ES) developed by Rechenberg [5] and Schwefel [6] EAs operate on a population of potential solutions, applying the principle of survival of the fittest to produce successively better approximations to a solution. At each generation of the EA, a new set of ....
L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence Through Simulated Evolution. New York: Wiley, 1966.
Approaches to Combining Local and Evolutionary Search for.. - Ku, Mak, Siu (Correct)
....Because of these difficulties, non gradient based searching approaches such as evolutionary search have been proposed. 1.2 Training by Evolutionary Search Another school of thought to train neural networks is to use evolutionary search. Genetic algorithms [23,54,56] evolutionary programming [18,20], and evolution strategies [67,69,73] are typical examples of evolutionary search. Attempts at training feedforward neural networks by evolutionary search include the work of Fogel et al. 19] Yao and Liu [87] and Montana and Davis [57] There are also attempts to evolve recurrent networks, e.g. ....
L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence Through Simulated Evolution. New York: Wiley, 1966.
Evolutionary Programming Made Faster - Yao, Liu, Lin (1999) (13 citations) (Correct)
....IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested. Index Terms Cauchy mutations, evolutionary programming, mixing operators. I. INTRODUCTION A LTHOUGH evolutionary programming (EP) was first proposed as an approach to artificial intelligence [1], it has been recently applied with success to many numerical and combinatorial optimization problems [2] 4] Optimization by EP can be summarized into two major steps: 1) mutate the solutions in the current population; 2) select the next generation from the mutated and the current solutions. ....
L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence Through Simulated Evolution. New York: Wiley, 1966.
Evolving Artificial Neural Networks - Yao (1999) (66 citations) (Correct)
....rule [7] More detailed discussion of ANN s can be found in [7] B. EA s EA s refer to a class of population based stochastic search algorithms that are developed from ideas and principles of natural evolution. They include evolution strategies (ES) 8] 9] evolutionary programming (EP) [10], 11] 12] and genetic algorithms (GA s) 13] 14] One important feature of all these algorithms is their populationbased search strategy. Individuals in a population compete and exchange information with each other in order to perform certain tasks. A general framework of EA s can be ....
L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence Through Simulated Evolution. New York: Wiley, 1966.
Strategical Diversity and Self Adaptive Behavior in.. - Takashina, Yoriki.. (Correct)
....information = 11010] Figure 1: An example of perceived information 1 0,1 input state output 0 1 sO s2 s1 eat s1 s1 s1 runaway s2 s3 sl eat s3 s3 s3 eat Figure 2: Sample of finite state automata representing strategy 2.1. 2 Genetic Operation Genetic operation to finite state automata [4] is an variation of simple GA. Chromosome corresponds to a transition table. Table i shows the case in which three operations are applied. Table 1: Genetic operation to finite state automata Mutation In a state selected randomly, one destination or output alphabet is changed randomly. ....
L. J. Fogel, A. J. Owens, and M. J. Walsh. Artifi- cial Intelligence Through Simulated Evolution. John Wiley 2z Sons, 1967.
Models for Evolutionary Algorithms and Their Applications in.. - Ursem (Correct)
....Initialization and evaluation 6 4 9 6 2 3 Figure 1.1: Initialization and the iterative cycle in evolutionary algorithms. Historically, EAs were first suggested in the 1940 ties [51] However, the founding fathers of modern EAs are considered to be Lawrence Fogel (Evolutionary Programming [53]) Ingo Rechenberg and Hans Paul Schwefel (Evolution Strategies [113] and by John Holland (Genetic Algorithms [68] Several years later, Evolutionary Algorithms (EAs) and Evolutionary Computation (EC) were introduced as unifying terms for the forest of optimization techniques inspired by ....
....evolve a mathematical expression. EAs evolving expressions are usually called Genetic Programming (GP) in the literature. In GP, the evolved expressions act as problem solvers rather than particular problem solutions. This idea is closely related to the much older idea of Evolutionary Programming [53], which is an approach for evolving automata that can learn symbolic patterns. The key data structure in GP is the parse tree representation. A parse tree consists of terminals and non terminals. The terminals are the leaves of the tree, while the non terminals are the nodes. The terminals may be ....
Fogel, L. J., Owens, A. J., and Walsh, M. J. (1966). Artificial Intelligence through Simulated Evolution. John Wiley & Sons.
Genetic Algorithms and Evolutionary Computing - Whitley (2002) (Correct)
....as a ( Gamma ES. This strategy is also used in what has come to be known as steady state genetic algorithms [14] Evolutionary Programming as practiced today is a reincarnation of earlier evolutionary computing methods developed by Lawrence Fogel, A.J. Owens and M.J. Walsh in the 1960 s [5]. During the 1960 s evolutionary programming used mutation to change finite state machines. The main idea behind evolutionary programming is to search in phenotype (or behavior) space rather than searching in genotype space (the space of genes that indirectly control for behavior after ....
L.J. Fogel, Owens A.J., and M.J. Walsh. Artificial Intelligence Through Simulated Evolution. John Wiley, 1966.
Distributed Beagle: An Environment for Parallel and.. - Gagne, Parizeau.. (2003) (Correct)
....ES can also be nested. GA GP Other EC Generic EC framework Object oriented foundations C Standard Template Library (STL) Figure 2. Open BEAGLE Framework Architecture. Evolutionary programming (EP) has been developed by L.J. Fogel in the 1960s and later by D.B. Fogel et al. in the 1990s [12,1]. EP was initially designed to evolve finite state machines and has been later extended to parameter optimization problems. The approach is more focused on the relation between parents and o#springs than on the simulation of natureinspired genetic operators. Contrary to the three first EC flavors, ....
L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. John Wiley & Sons, New York, 1966.
Coevolutionary Fuzzy Modeling - Peña-Reyes (2002) (Correct)
.... approaches to problem solving date back to the late 1950s [16, 17,44,49, 50] Independent and almost simultaneous research conducted by Rechenberg and Schwefel on evolution strategies [147,148,157,158] by Holland on genetic algorithms [62, 64] and by Fogel on evolutionary programming [47, 48] triggered the study and the application of evolutionary techniques. Three basic mechanisms drive natural evolution: reproduction, mutation,andselection. These mechanisms act on the chromosomes containing the genetic information of the individual (the genotype) rather than on the individual ....
....algorithm. Later, recombination was added as evolution strategies were extended to encompass populations of individuals. A good source for further information on evolution strategies is the book by Schwefel [159] 1.3. 4 Evolutionary programming Lawrence Fogel proposed evolutionary programming [47, 48] as a means to develop artificial intelligence. He argued that intelligent behavior requires both the ability to predict changes in an environment, and a translation of the predictions into actions appropriate for reaching a goal. In its most general, the environment is described as a sequence of ....
L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. John Wiley & Sons, New York, 1966.
Genetic Programming Approach to Extracting.. - Brumby, Theiler.. (2001) (1 citation) (Correct)
.... Keywords: Evolutionary Computation, Genetic Programming, Image Processing, Remote Sensing, Multispectral Imagery, Panchromatic imagery 1 Genetic programming with supervised classification GENIE [1 4] is an evolutionary computation (EC) software system, using a genetic algorithm (GA) [5 7] to assemble image processing algorithms from a collection of low level ( primitive ) image processing operators (e.g. edge detectors, texture measures, spectral operations, and various morphological filters) This system has been shown to be effective in looking for complex terrain features, ....
L. Fogel, A. Owens and M. Walsh, Artificial Intelligence through Simulated Evolution, Wiley, New York, 1966.
Noisy Optimization Problems - A Particular Challenge.. - Krink, Filipic.. (2004) Self-citation (Fogel) (Correct)
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FOGEL, L. J., OWENS, A. J., AND WALSH, M. J. Artificial Intelligence through Simulated Evolution. John Wiley & Sons, New York, 1966.
submitted to Machine Learning, , 1--25 () c - Stimulus Response Learning (Correct)
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L.J. Fogel, A.J. Owens, and M.J. Walsh. Artificial Intelligence through Simulated Evolution. Wiley, New York, 1966.
Multi-Cellular Reconfigurable Circuits: Evolution Morphogenesis.. - Roggen (2005) (Correct)
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L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence Through Simulated Evolution. John Wiley, Chichester, UK, 1966.
Artificial Intelligence 170 (2006) 953--982 - Www Elsevier Com (2006) (1 citation) (Correct)
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L.J. Fogel, A.J. Owens, M.J. Walsh, Artificial Intelligence through Simulated Evolution, Wiley, New York, 1966.
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L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. John Wiley & Sons, New York, 1966.
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L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial intelligence through simulated evolution. Wiley, New York, 1966.
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L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. Wiley, New York, 1966.
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Fogel, L.J., Owens, A.J. and Walsh, M.J. (1967) Artificial Intelligence Through Simulated Evolution, John Wiley & Sons, New Yo r k .
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Fogel, L.J., Owens, A.J. and Walsh, M.J. (1966) Artificial Intelligence Through Simulated Evolution, John Wiley & Sons, New Yo r k Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Corporation, Inc, Reading, MA.
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Fogel, L.J., Owens, A.J. and Walsh, M.J., Artificial Intelligence Through Simulated Evolution, John Wiley & Sons Inc. USA, 1966.
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L. Fogel, A. Owens, and M. Walsh, Artificial intelligence through simulated evolution, Wiley, New York, 1966.
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L. Fogel, A. Owens, M. Walsh, Artificial Intelligence Through Simulated Evolution, Wiley, New York NY, 1966.
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Fogel L.J., Owens A.J. and Walsh M.J. Artificial Intelligence through Simulated Evolution, Wiley, New York, 1966.
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Lawrence J. Fogel, Alvin J. Owens, and Michael J. Walsh. Artificial Intelligence Through Simulated Evolution. John Wiley and Sons, Inc., New York, 1966.
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Fogel L.J., Owens A.J., Walsh M.J. "Artificial intelligence through simulated evolution" New York: John Wiley, 1966 94
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L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence through Simulated Evolution. New York: John Wiley & Sons, 1966.
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L.J. Fogel, A.J. Owens, and M.J. Walsh, Artificial Intelligence Through Simulated Evolution, John Wiley & Sons, New York, 1966.
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L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence Through Simulated Evolution. New York: Wiley, 1966.
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L. Fogel, A. Owens, and M. Walsh. Artificial Intelligence Through Simulated Evolution. Wiley, New York, 1966. 149
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Alvin J. Owens Lawrence J. Fogel and Michael J. Walsh. Artificial Intelligence Through Simulated Evolution. Wiley, 1966.
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L.J. Fogel, A.J. Owens, and M.J. Walsh. Artificial Intelligence through Simulated Evolution. Wiley, New York, 1966.
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Fogel, L.J., Owens, A.J. and Walsh, M.J. (1966). Artificial Intelligence through Simulated Evolution, New York: John Wiley & Sons.
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L. J. Forgel, A.J. Owens e M.J. Walsh. "Artificial Intelligence through Simulated Evolution". New York: Wiley Publishing. 1966.
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L.J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence through Simulated Evolution, New York: John Wiley, 1966.
An Evolutionary Based Approach for Control Programming of.. - Wolff, Nordin (2003) (Correct)
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Fogel, L. J., Owens, A. J., and Walsh, M. J. 1966: Artificial Intelligence through Simulated Evolution. New York, USA: Wiley.
Distributed Beagle: An Environment for Parallel and.. - Gagne, Parizeau.. (2003) (Correct)
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L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. John Wiley & Sons, New York, 1966.
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L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence Through Simulated Evolution, John Wiley & Sons, New York, NY, 1966.
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L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence Through Simulated Evolution. New York, NY: John Wiley & Sons, 1966.
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L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. John Wiley & Sons, New York, NY, 1966.
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L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence through Simulated Evolution, John Wiley: New York, 1966.
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