07-02-2012, 03:00 PM
ant colony optimization
[attachment=17247]
INTRODUCTION
1.1 BACK GROUND AND RELATED WORK.
Genetic Algorithms (GA) have been used to evolve computer programs for specific tasks, and to design other computational structures. The recent resurgence of interest in AP with GA has been spurred by the work on Genetic Programming (GP). GP paradigm provides a way to do program induction by searching the space of possible computer programs for an individual computer program that is highly fit in solving or approximately solving the problem at hand. The genetic programming paradigm permits the evolution of computer programs which can perform alternative computations conditioned on the outcome of intermediate calculations, which can perform computations on variables of many different types, which can perform iterations and recursions to achieve the desired result, which can define and subsequently use computed values and subprograms, and whose size, shape, and complexity is not specified in advance. GP use relatively low-level primitives, which are defined separately rather than combined a priori into high-level primitives, since such mechanism generate hierarchical structures that would facilitate the creation of new high-level primitives from built-in low-level primitives. Unfortunately, since every real life problem are dynamic problem, thus their behaviors are much complex, GP suffers from serious weaknesses. Random systems chaos is important, in part, because it helps us to cope with unstable system by improving our ability to describe, to understand, perhaps even to forecast them.
GENETIC PROGRMMING.
Some specific advantages of genetic programming are that no analytical knowledge is needed and still could get accurate results. GP approach does scale with the problem size. GP does impose restrictions on how the structure of solutions should be formulated. There are several variants of GP, some of them are: Linear Genetic Programming (LGP), Gene Expression Programming (GEP), Multi Expression Programming (MEP), Cartesian Genetic Programming (CGP), Traceless Genetic Programming (TGP) and Genetic Algorithm for Deriving Software (GADS).Cartesian Genetic Programming was originally developed by Miller and Thomson for the purpose of evolving digital circuits and represents a program as a directed graph. One of the benefits of this type of representation is the implicit re-use of nodes in the directed graph.
SWARM INTELLIGENCE
Swarm intelligence (SI) describes the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
ANT COLONY
The complex social behaviours of ants have been much studied by science, and computer scientists are now finding that these behaviour patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behaviour, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behaviour. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses.
[attachment=17247]
INTRODUCTION
1.1 BACK GROUND AND RELATED WORK.
Genetic Algorithms (GA) have been used to evolve computer programs for specific tasks, and to design other computational structures. The recent resurgence of interest in AP with GA has been spurred by the work on Genetic Programming (GP). GP paradigm provides a way to do program induction by searching the space of possible computer programs for an individual computer program that is highly fit in solving or approximately solving the problem at hand. The genetic programming paradigm permits the evolution of computer programs which can perform alternative computations conditioned on the outcome of intermediate calculations, which can perform computations on variables of many different types, which can perform iterations and recursions to achieve the desired result, which can define and subsequently use computed values and subprograms, and whose size, shape, and complexity is not specified in advance. GP use relatively low-level primitives, which are defined separately rather than combined a priori into high-level primitives, since such mechanism generate hierarchical structures that would facilitate the creation of new high-level primitives from built-in low-level primitives. Unfortunately, since every real life problem are dynamic problem, thus their behaviors are much complex, GP suffers from serious weaknesses. Random systems chaos is important, in part, because it helps us to cope with unstable system by improving our ability to describe, to understand, perhaps even to forecast them.
GENETIC PROGRMMING.
Some specific advantages of genetic programming are that no analytical knowledge is needed and still could get accurate results. GP approach does scale with the problem size. GP does impose restrictions on how the structure of solutions should be formulated. There are several variants of GP, some of them are: Linear Genetic Programming (LGP), Gene Expression Programming (GEP), Multi Expression Programming (MEP), Cartesian Genetic Programming (CGP), Traceless Genetic Programming (TGP) and Genetic Algorithm for Deriving Software (GADS).Cartesian Genetic Programming was originally developed by Miller and Thomson for the purpose of evolving digital circuits and represents a program as a directed graph. One of the benefits of this type of representation is the implicit re-use of nodes in the directed graph.
SWARM INTELLIGENCE
Swarm intelligence (SI) describes the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
ANT COLONY
The complex social behaviours of ants have been much studied by science, and computer scientists are now finding that these behaviour patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behaviour, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behaviour. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses.
