scholarly journals Genetic Programming Hyper-heuristic for the Automated Synthesis of Selection Operators in Genetic Algorithms

2017 ◽  
Author(s):  
Evgenii Sopov
Keyword(s):  
Genetic Algorithms ◽  
Genetic Programming ◽  
Automated Synthesis ◽  
Selection Operators

scholarly journals A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms

2007 ◽  
pp. 1-11 ◽  
Author(s):  
Erik Hemberg ◽  
Conor Gilligan ◽  
Michael O’Neill ◽  
Anthony Brabazon
Keyword(s):  
Genetic Algorithms ◽  
Genetic Programming ◽  
Programming Approach

Evolving Bots’ AI in Unreal™

2012 ◽  
pp. 134-157 ◽  
Author(s):  
Antonio M. Mora-García ◽  
Juan Julián Merelo-Guervós
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Genetic Programming ◽  
Evolutionary Methods ◽  
Human Player

A bot is an autonomous enemy which tries to beat the human player and/or some other bots in a game. This chapter describes the design, implementation and results of a system to evolve bots inside the PC game Unreal™. The default artificial intelligence (AI) of this bot has been improved using two different evolutionary methods: genetic algorithms (GAs) and genetic programming (GP). The first one has been applied for tuning the parameters of the hard-coded values inside the bot AI code. The second method has been used to change the default set of rules (or states) that defines its behaviour. Moreover, the first approach has been considered at two levels: individual and team, performing different studies at the latter level, looking for the best cooperation scheme. Both techniques yield very good results, evolving bots (and teams) which are capable of defeating the default ones. The best results are obtained for the GA approach, since it just performs a refinement considering the default behaviour rules, while the GP method has to redefine the whole set of rules, so it is harder to get good results. This chapter presents one possibility of AI programming: building a better model from a standard one.

Performance of genetic algorithms with different selection operators for solving short-term optimized reservoir scheduling problem

2019 ◽  
Vol 24 (9) ◽  
pp. 6771-6785
Author(s):  
Ling Shang ◽  
Yizi Shang ◽  
Lianxing Hu ◽  
Jianlin Li
Keyword(s):  
Genetic Algorithms ◽  
Scheduling Problem ◽  
Short Term ◽  
Selection Operators

Conference on Genetic Programming (GP-97)

Robotica ◽  
1998 ◽  
Vol 16 (1) ◽  
pp. 117-118
Author(s):  
W. B. Langdon
Keyword(s):  
Genetic Algorithms ◽  
Genetic Programming ◽  
Dna Computing ◽  
Evolvable Hardware ◽  
Stanford University ◽  
University Campus ◽  
Classifier Systems ◽  
International Conference ◽  
The World ◽  
Second International

The Second International Conference in Genetic Programming (GP-97), like the first, was held on the beautiful Stanford University campus in California under the chairmanship of John Koza. More than 350 people from all over the world gathered together for four days to see presentations, posters, tutorials and trade presentations on a range of topics. In addition to GP and related topics in Genetic Algorithms (GAs) and classifier systems, the up and coming fields of evolvable hardware (EHW) and biocomputing (also called DNA computing) were also represented.

Automated synthesis of passive filter circuits including parasitic effects by genetic programming

2006 ◽  
Vol 37 (8) ◽  
pp. 792-799
Author(s):  
Shoou-Jinn Chang ◽  
Hao-Sheng Hou ◽  
Yan-Kuin Su
Keyword(s):  
Genetic Programming ◽  
Automated Synthesis ◽  
Passive Filter ◽  
Parasitic Effects

scholarly journals Evolutionary Computation Techniques for Predicting Atmospheric Corrosion

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Amine Marref ◽  
Saleh Basalamah ◽  
Rami Al-Ghamdi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Corrosion Rate ◽  
Genetic Programming ◽  
Atmospheric Corrosion ◽  
Structural Integrity ◽  
Good Accuracy ◽  
Residual Life ◽  
Vital Role ◽  
Engineering Structures

Corrosion occurs in many engineering structures such as bridges, pipelines, and refineries and leads to the destruction of materials in a gradual manner and thus shortening their lifespan. It is therefore crucial to assess the structural integrity of engineering structures which are approaching or exceeding their designed lifespan in order to ensure their correct functioning, for example, carrying ability and safety. An understanding of corrosion and an ability to predict corrosion rate of a material in a particular environment plays a vital role in evaluating the residual life of the material. In this paper we investigate the use of genetic programming and genetic algorithms in the derivation of corrosion-rate expressions for steel and zinc. Genetic programming is used to automatically evolve corrosion-rate expressions while a genetic algorithm is used to evolve the parameters of an already engineered corrosion-rate expression. We show that both evolutionary techniques yield corrosion-rate expressions that have good accuracy.

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