Results 271 to 280 of about 116,877 (316)
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Genetic Improvement of Genetic Programming
2020 IEEE Congress on Evolutionary Computation (CEC), 2020GISMOE BNF grammar based GI is applied to optimise run time of the tree interpreter in the fastest single computer floating point genetic programming system, GPavx. Up to two fold speed up is obtained. Performance varies with tree size. The GI version of Singleton’s C++ GPquick is demonstrated on random trees of up to 79 million opcodes on Intel AVX512
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On the automaticity of genetic programming
14th International Conference on Electronics, Communications and Computers, 2004. CONIELECOMP 2004., 2004Genetic/evolutionary algorithms, based upon an analogy to the mechanics of Mendelian genetics and Darwinian evolutionary theory, offer an automatic way to improve programs. The process cycles many times, selecting for reproduction among a population of program variants (represented by the "chromosomes") to form the next generation, with "mutation" and "
Melvin Neville +2 more
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Expressive Genetic Programming
Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2013The language in which evolving programs are expressed can have significant impacts on the problem-solving capabilities of a genetic programming system. These impacts stem both from the absolute computational power of the languages that are used, as elucidated by formal language theory, and from the ease with which various computational structures can ...
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Reinforced Genetic Programming
Genetic Programming and Evolvable Machines, 2001Summary: This paper introduces the Reinforced Genetic Programming (RGP) system, which enhances standard tree-based genetic programming (GP) with reinforcement learning (RL). RGP adds a new element to the GP function set: monitored action-selection points that provide hooks to a reinforcement-learning system.
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Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007
Genetic Programming (GP) is a complex adaptive system with an immens number of degrees of freedom. Understanding how, why and when it work is difficult. Its behaviour is typically investigated in two ways experimentally and theoretically. Experimental studies require the experimenter to choose which problems, parameter settings and descriptors to use ...
Riccardo Poli, William B. Langdon
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Genetic Programming (GP) is a complex adaptive system with an immens number of degrees of freedom. Understanding how, why and when it work is difficult. Its behaviour is typically investigated in two ways experimentally and theoretically. Experimental studies require the experimenter to choose which problems, parameter settings and descriptors to use ...
Riccardo Poli, William B. Langdon
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Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, 2011
Genetic programming emerged in the early 1990's as one of the most exciting new evolutionary algorithm paradigms. It has rapidly grown into a thriving area of research and application. While sharing the evolutionary inspired algorithm principles of a genetic algorithm, it differs by exploiting an executable genome.
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Genetic programming emerged in the early 1990's as one of the most exciting new evolutionary algorithm paradigms. It has rapidly grown into a thriving area of research and application. While sharing the evolutionary inspired algorithm principles of a genetic algorithm, it differs by exploiting an executable genome.
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Genetic Programming Multitasking
2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020In this paper, we present a new multitasking algorithm for Genetic Programming (GP). Our proposed algorithm (referred to as “GP-Tasking”) evolves population using multifaceted strategy. Each individual is trained with different training sets and evaluated with multiple fitness functions (where each fitness function represents one task).
Ahmed Kattan +3 more
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Soft Computing, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Piotr Wasiewicz, Jan J. Mulawka
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Piotr Wasiewicz, Jan J. Mulawka
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Schemas and Genetic Programming
2000To investigate the mechanisms which enable systems to learn is among the most challenging of research activities. In computer science alone it is pursued by at least three communities (Carbonel 1990; Natarajan 1991; Ritter et al. 1991). The overwhelming majority of all studies treats situations with strong inductive bias, i.e.
Birk, Andreas, Paul, Wolfgang
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2000
This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers. The inputs or terminal set and node outputs are numbered sequentially. The node functions are also separately numbered.
Julian F. Miller, Peter Thomson
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This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers. The inputs or terminal set and node outputs are numbered sequentially. The node functions are also separately numbered.
Julian F. Miller, Peter Thomson
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