Information about Evolutionary Methods
In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, natural selection and survival of the fittest. Candidate solutions to the optimization problem play the role of individuals in a population, and the cost function determines the environment within which the solutions "live" (see also fitness function). Evolution of the population then takes place after the repeated application of the above operators. Artificial evolution (AE) describes a process involving individual evolutionary algorithms; EAs are individual components that participate in an AE.
Evolutionary algorithms consistently perform well approximating solutions to all types of problems because they do not make any assumption about the underlying fitness landscape; this generality is shown by successes in fields as diverse as engineering, art, biology, economics, genetics, operations research, robotics, social sciences, physics, and chemistry.
Apart from their use as mathematical optimizers, evolutionary computation and algorithms have also been used as an experimental framework within which to validate theories about biological evolution and natural selection, particularly through work in the field of artificial life. Techniques from evolutionary algorithms applied to the modelling of biological evolution are generally limited to explorations of microevolutionary processes, however some computer simulations, such as Tierra and Avida, attempt to model macroevolutionary dynamics.
A limitation of evolutionary algorithms is their lack of a clear genotype-phenotype distinction. In nature, the fertilized egg cell undergoes a complex process known as embryogenesis to become a mature phenotype. This indirect encoding is believed to make the genetic search more robust (i.e. reduce the probability of fatal mutations), and also may improve the evolvability of the organism. Recent work in the field of artificial embryogeny, or artificial developmental systems, seeks to address these concerns.
In selection, parents for the next generation are chosen with a bias towards higher fitness. The parents reproduce by copying with recombination and/or mutation. Recombination acts on the two selected parents (candidates) and results in one or two children (new candidates). Mutation acts on one candidate and results in a new candidate. These operators create the offspring (a set of new candidates). These new candidates compete with old candidates for their place in the next generation (survival of the fittest).
This process can be repeated until a candidate with sufficient quality (a solution) is found or a previously determined computational limit is reached.
Natural selection is the process by which favorable traits that are heritable become more common in successive generations of a population of reproducing organisms, and unfavorable traits that are heritable become less
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Natural selection is the process by which favorable traits that are heritable become more common in successive generations of a population of reproducing organisms, and unfavorable traits that are heritable become less
..... Click the link for more information.
Evolutionary algorithms consistently perform well approximating solutions to all types of problems because they do not make any assumption about the underlying fitness landscape; this generality is shown by successes in fields as diverse as engineering, art, biology, economics, genetics, operations research, robotics, social sciences, physics, and chemistry.
Apart from their use as mathematical optimizers, evolutionary computation and algorithms have also been used as an experimental framework within which to validate theories about biological evolution and natural selection, particularly through work in the field of artificial life. Techniques from evolutionary algorithms applied to the modelling of biological evolution are generally limited to explorations of microevolutionary processes, however some computer simulations, such as Tierra and Avida, attempt to model macroevolutionary dynamics.
A limitation of evolutionary algorithms is their lack of a clear genotype-phenotype distinction. In nature, the fertilized egg cell undergoes a complex process known as embryogenesis to become a mature phenotype. This indirect encoding is believed to make the genetic search more robust (i.e. reduce the probability of fatal mutations), and also may improve the evolvability of the organism. Recent work in the field of artificial embryogeny, or artificial developmental systems, seeks to address these concerns.
Implementation of biological processes
Usually, an initial population of randomly generated candidate solutions comprise the first generation. The fitness function is applied to the candidate solutions and any subsequent offspring. Two main classes of fitness functions exist: one where the fitness function does not change, as in optimizing a fixed function or testing with a fixed set of test cases; and one where the fitness function is mutable, as in using niche differentiation or co-evolving the set of test cases.In selection, parents for the next generation are chosen with a bias towards higher fitness. The parents reproduce by copying with recombination and/or mutation. Recombination acts on the two selected parents (candidates) and results in one or two children (new candidates). Mutation acts on one candidate and results in a new candidate. These operators create the offspring (a set of new candidates). These new candidates compete with old candidates for their place in the next generation (survival of the fittest).
This process can be repeated until a candidate with sufficient quality (a solution) is found or a previously determined computational limit is reached.
Evolutionary algorithm techniques
Similar techniques differ in the implementation details and the nature of the particular applied problem.- Genetic algorithm - This is the most popular type of EA. One seeks the solution of a problem in the form of strings of numbers (traditionally binary, although the best representations are usually those that reflect something about the problem being solved - these are not normally binary), virtually always applying recombination operators in addition to selection and mutation. This type of EA is often used in optimization problems;
- Genetic programming - Here the solutions are in the form of computer programs, and their fitness is determined by their ability to solve a computational problem.
- Evolutionary programming - Like genetic programming, only the structure of the program is fixed and its numerical parameters are allowed to evolve;
- Evolution strategy - Works with vectors of real numbers as representations of solutions, and typically uses self-adaptive mutation rates;
- Learning classifier system - Instead of using a fitness function, rule utility is decided by a reinforcement learning technique.
Related techniques
- Differential evolution - Based on vector differences and is therefore primarily suited for numerical optimization problems.
- Particle swarm optimization - Based on the ideas of animal flocking behaviour. Also primarily suited for numerical optimization problems.
- Ant colony optimization - Based on the ideas of ant foraging by pheromone communication to form path. Primarily suited for combinatorial optimization problems.
- Invasive weed optimization algorithm - Based on the ideas of weed colony behavior in searching and finding a suitable place for growth and reproduction.
- Harmony search - Based on the ideas of musicians behavior in searching for better harmonies. This algorithm is suitable for combinatorial optimization as well as parameter optimization.
- Gaussian adaptation - Based on information theory. Used for maximization of manufacturing yield, mean fitness or average information. See for instance Entropy in thermodynamics and information theory.
Bibliography
- Ingo Rechenberg (1971): Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis). Reprinted by Fromman-Holzboog (1973).
- Hans-Paul Schwefel (1974): Numerische Optimierung von Computer-Modellen (PhD thesis). Reprinted by Birkhäuser (1977).
- Bäck, T. (1996), Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford Univ. Press.
- Bäck, T., Fogel, D., Michalewicz, Z. (1997), Handbook of Evolutionary Computation, Oxford Univ. Press.
- Eiben, A.E., Smith, J.E. (2003), Introduction to Evolutionary Computing, Springer.
- Holland, J. H. (1975), Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor
- Ashlock, D. (2006), Evolutionary Computation for Modeling and Optimization, Springer, ISBN 0-387-22196-4.
See also
- Developmental biology
- Evolutionary computation
- Evolutionary robotics
- Fitness landscape
- Genetic operators
- Interactive evolutionary computation
- Program synthesis
External links
- Demo applet of a evolutionary algorithm for solving TSP's and VRPTW problems
- International Society for Genetic and Evolutionary Computation - Home of the largest conference on the subject, GECCO (since January 1, 2005 reorganized to become Special Interest Group for Genetic and Evolutionary Computation of the ACM)
- Illinois Genetic Algorithms Laboratory (IlliGAL)
- Kanpur Genetic Algorithms Laboratory (KanGAL)
- Evolutionary Computation Repository
- Evolutionary Computing European Network Of Excellence
- Hitch-Hiker's Guide to Evolutionary Computation (FAQ for comp.ai.genetic)
- Evolutionary computation in manufacturing scheduling
- www.talkorigins.org: Genetic Algorithms and Evolutionary Computation
- Evolutionary Computation Lab at George Mason University (ECLab)
- CILib - GPLed computational intelligence simulation and research environment written in Java, includes various EC implementations
- JAGA - Extensible and pluggable open source API for implementing genetic algorithms and genetic programming applications (Java)
- - A very tiny (less than 1000 lines of C!) evolvable instruction set virtual machine that exhibits striking emergent behaviors.
- Evolving Objects - GPLed C++ framework for evolutionary computation
- E-Book - Global Optimization Algorithms - Theory and Application
- Economist article - a showcase of real-world EA examples
artificial intelligence (or AI) is "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.
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subset of a set B if A is "contained" inside B. Notice that A and B may coincide. The relationship of one set being a subset of another is called inclusion or containment.
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In computer science evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) that involves combinatorial optimization problems.
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A metaheuristic is a heuristic method for solving a very general class of computational problems by combining user given black-box procedures — usually heuristics themselves — in a hopefully efficient way.
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In mathematics, the term optimization, or mathematical programming, refers to the study of problems in which one seeks to minimize or maximize a real function by systematically choosing the values of real or integer variables from within an allowed set.
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In mathematics, computing, linguistics, and related disciplines, an algorithm is a finite list of well-defined instructions for accomplishing some task that, given an initial state, will proceed through a well-defined series of successive states, eventually terminating in an
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mutations are changes to the base pair sequence of the genetic material of an organism. Mutations can be caused by copying errors in the genetic material during cell division, by exposure to ultraviolet or ionizing radiation, chemical mutagens, or viruses, or can occur deliberately
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Recombination may refer to:
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- Recombination (biology)
- Genetic recombination
- Carrier generation and recombination, processes by which mobile electrons and electron holes are created and eliminated
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Natural selection is the process by which favorable traits that are heritable become more common in successive generations of a population of reproducing organisms, and unfavorable traits that are heritable become less
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Survival of the fittest is a phrase which is a shorthand for a concept relating to competition for survival or predominance. Originally applied by Herbert Spencer in his Principles of Biology
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In optimization (a branch of mathematics), a candidate solution is a member of a set of possible solutions to a given problem. A candidate solution does not have to be a likely or reasonable solution to the problem.
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In mathematics, the term optimization, or mathematical programming, refers to the study of problems in which one seeks to minimize or maximize a real function by systematically choosing the values of real or integer variables from within an allowed set.
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A fitness function is a particular type of objective function that quantifies the optimality of a solution (that is, a chromosome) in a genetic algorithm so that that particular chromosome may be ranked against all the other chromosomes.
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In evolutionary biology, fitness landscapes or adaptive landscapes are used to visualize the relationship between genotypes (or phenotypes) and reproductive success. It is assumed that every genotype has a well defined replication rate (often referred to as fitness).
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Engineering is the applied science of acquiring and applying knowledge to design, analysis, and/or construction of works for practical purposes. The American Engineers' Council for Professional Development, also known as ECPD,[1] (later ABET [2]
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ART is a three-letter acronym that can mean:
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Medicine
- Antiretroviral therapy. It is used in the treatment of HIV infection.
- assisted reproductive technology
Other
- Adaptive resonance theory
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Biology (from Greek: βίος, bio, "life"; and λόγος, logos, "knowledge"), also referred to as the biological sciences, is the scientific study of life.
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Economics is the social science that studies the production, distribution, and consumption of goods and services. The term economics comes from the Greek for oikos (house) and nomos (custom or law), hence "rules of the house(hold).
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Genetics is the science of heredity and variation in living organisms.[1][2] Knowledge of the inheritance of characteristics has been implicitly used since prehistoric times for improving crop plants and animals through selective breeding.
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Operations Research or Operational Research (OR) is an interdisciplinary branch of mathematics which uses methods like mathematical modeling, statistics, and algorithms to arrive at optimal or good decisions in complex problems which are concerned with optimizing the maxima
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Evolutionary Robotics (ER) is a methodology that uses evolutionary computation to develop controllers for autonomous robots. Algorithms in ER frequently operate on populations of candidate controllers, initially selected from some distribution.
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The social sciences are a group of academic disciplines that study human aspects of the world. They diverge from the arts and humanities in that the social sciences tend to emphasize the use of the scientific method in the study of humanity, including quantitative and qualitative
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Physics is the science of matter[1] and its motion[2][3], as well as space and time[4][5] —the science that deals with concepts such as force, energy, mass, and charge.
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Natural selection is the process by which favorable traits that are heritable become more common in successive generations of a population of reproducing organisms, and unfavorable traits that are heritable become less
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Artificial Life, (commonly Alife or alife) is a field of study and associated art form which examine systems related to life, its processes and its evolution through simulations using computer models, robotics, and biochemistry[1].
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