scholarly journals Evolutionary Computation: from Genetic Algorithms to Genetic Programming

2006 ◽  
pp. 1-20
Author(s):  
Ajith Abraham ◽  
Nadia Nedjah ◽  
Luiza de Macedo Mourelle
Keyword(s):  
Genetic Algorithms ◽  
Genetic Programming ◽  
Evolutionary Computation

scholarly journals Conference Report

Robotica ◽  
2000 ◽  
Vol 18 (1) ◽  
pp. 87-87 ◽  
Author(s):  
W.B. Langdon
Keyword(s):  
Genetic Algorithms ◽  
Genetic Programming ◽  
Evolutionary Computation ◽  
Graduate Student ◽  
Artificial Life ◽  
Conference Report ◽  
Evolvable Hardware ◽  
Dna Computation ◽  
International Conference ◽  
Research Students

GECCO-99 STUDENT WORKSHOP(13th July, 1999)The Genetic and Evolutionary Computation Conference (GECCO-99) in Orlando, Florida, USA was the biggest ever conference on evolutionary computation. It was a joint meeting of the long standing biannual International Conference on Genetic Algorithms (ICGA) which has been running since 1985 and the more recent annual Genetic Programming (GP) conference (which started in 1996). Before the start of each GP conference there has been a workshop for PhD students research in GP. This year Dr. O'Reilly (MIT AI lab) organized a joint graduate student workshop for research students principally concerned with any aspect of evolutionary computation, Evolvable Hardware, DNA computation, Artificial Life or Agents.

On Foundations of Evolutionary Computation

2011 ◽  
pp. 342-360 ◽  
Author(s):  
Mark Burgin ◽  
Eugene Eberbach
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Evolutionary Computation ◽  
Finite Automata ◽  
Recursive Algorithms ◽  
Turing Machines ◽  
Evolutionary Computations ◽  
Cooperation And Competition ◽  
Mathematical Foundations

There are different models of evolutionary computations: genetic algorithms, genetic programming, etc. This chapter presents mathematical foundations of evolutionary computation based on the concept of evolutionary automaton. Different classes of evolutionary automata (evolutionary finite automata, evolutionary Turing machines and evolutionary inductive Turing machines) are introduced and studied. It is demonstrated that evolutionary algorithms are more expressive than conventional recursive algorithms, such as Turing machines. Universal evolutionary algorithms and automata are constructed. It is proved that classes of evolutionary finite automata, evolutionary Turing machines and evolutionary inductive Turing machines have universal automata. As in the case of conventional automata and Turing machines, universal evolutionary algorithms and automata provide means to study many important problems in the area of evolutionary computation, such as complexity, completeness, optimality and search decidability of evolutionary algorithms, as well as such natural phenomena as cooperation and competition. Expressiveness and generality of the introduced classes of evolutionary automata are investigated.

Evolutionary Computation and Genetic Algorithms

2011 ◽  
pp. 477-481
Author(s):  
William H. Hsu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Genetic Programming ◽  
Sexual Reproduction ◽  
Evolutionary Computation ◽  
Evolutionary Biology ◽  
Approximate Solutions ◽  
Biologically Inspired ◽  
Genetic Inheritance

A genetic algorithm (GA) is a procedure used to find approximate solutions to search problems through the application of the principles of evolutionary biology. Genetic algorithms use biologically inspired techniques, such as genetic inheritance, natural selection, mutation, and sexual reproduction (recombination, or crossover). Along with genetic programming (GP), they are one of the main classes of genetic and evolutionary computation (GEC) methodologies.

scholarly journals Evolutionary Algorithms for Boolean Functions in Diverse Domains of Cryptography

2016 ◽  
Vol 24 (4) ◽  
pp. 667-694 ◽  
Author(s):  
Stjepan Picek ◽  
Claude Carlet ◽  
Sylvain Guilley ◽  
Julian F. Miller ◽  
Domagoj Jakobovic
Keyword(s):  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Evolutionary Computation ◽  
Boolean Functions ◽  
Coding Theory ◽  
Design Criteria ◽  
Hamming Weight ◽  
High Quality ◽  
Distinct Role

The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography. The first uses Boolean functions as the source of the nonlinearity in filter and combiner generators. Although relatively well explored using evolutionary algorithms, it still presents an interesting goal in terms of the practical sizes of Boolean functions. The second scenario appeared rather recently where the objective is to find Boolean functions that have various orders of the correlation immunity and minimal Hamming weight. In both these scenarios we see that evolutionary algorithms are able to find high-quality solutions where genetic programming performs the best.

Efficient Evolution of Modular Robot Control via Genetic Programming

2013 ◽  
pp. 59-85
Author(s):  
Tüze Kuyucu ◽  
Ivan Tanev ◽  
Katsunori Shimohara
Keyword(s):  
Multiobjective Optimization ◽  
Genetic Programming ◽  
Evolutionary Computation ◽  
Fast Moving ◽  
Search Space ◽  
Multiple Objectives ◽  
Experimental Results ◽  
Robotic Systems ◽  
Linear Fashion ◽  
Fast Evolution

In Genetic Programming (GP), most often the search space grows in a greater than linear fashion as the number of tasks required to be accomplished increases. This is a cause for one of the greatest problems in Evolutionary Computation (EC): scalability. The aim of the work presented here is to facilitate the evolution of control systems for complex robotic systems. The authors use a combination of mechanisms specifically designed to facilitate the fast evolution of systems with multiple objectives. These mechanisms are: a genetic transposition inspired seeding, a strongly-typed crossover, and a multiobjective optimization. The authors demonstrate that, when used together, these mechanisms not only improve the performance of GP but also the reliability of the final designs. They investigate the effect of the aforementioned mechanisms on the efficiency of GP employed for the coevolution of locomotion gaits and sensing of a simulated snake-like robot (Snakebot). Experimental results show that the mechanisms set forth contribute to significant increase in the efficiency of the evolution of fast moving and sensing Snakebots as well as the robustness of the final designs.

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.

Soft Computing Techniques in Civil Engineering

2010 ◽  
pp. 143-159
Author(s):  
Juan L. Pérez ◽  
Juan R. Rabuñal ◽  
Fernando Martínez Abella
Keyword(s):  
Genetic Programming ◽  
Evolutionary Computation ◽  
Soft Computing ◽  
Civil Engineering ◽  
Structural Concrete ◽  
Controlled Conditions ◽  
Classical Algorithm ◽  
Soft Computing Techniques ◽  
Over Time

Soft computing techniques are applied to a huge quantity of problems spread in several areas of science. In this case, Evolutionary Computation (EC) techniques are applied, in concrete Genetic Programming (GP), to a temporary problem associated to the field of Civil Engineering. The case of study of this technique has been centered in the prediction, over time, of the behavior of the structural concrete in controlled conditions. Given the temporary nature of the case of study, it has been necessary to make several changes to the classical algorithm of GP, among whom it can be emphasized the incorporation of a new operator that gives the GP the ability to be able to solve problems with temporary behavior. The obtained results shown that the proposed method has succeeded in improving the adjustment to the current regulations about creep in the structural concrete.

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