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The synuclein family. What do we mean by Open-Ended Evolution? : oee. Exploring the Concept of Open-Ended Evolution | Tim Taylor. Evolutionary computation. In computer science, evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) that involves continuous optimization and combinatorial optimization problems. Its algorithms can be considered global optimization methods with a metaheuristic or stochastic optimization character and are mostly applied for black box problems (no derivatives known), often in the context of expensive optimization. Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution. As evolution can produce highly optimised processes and networks, it has many applications in computer science.

History[edit] The use of Darwinian principles for automated problem solving originated in the 1950s. Evolutionary programming was introduced by Lawrence J. D. Genetic programming. In artificial intelligence, genetic programming (GP) is a technique whereby computer programs are encoded as a set of genes that are then modified (evolved) using an evolutionary algorithm (often a genetic algorithm - "GA").

The result is a computer program able to perform well in a predefined task. Often confused to be a kind of genetic algorithm, GP can indeed be seen as an application of genetic algorithms to problems where each individual is a computer program. The methods used to encode a computer program in an artificial chromosome and to evaluate its fitness with respect to the predefined task are central in the GP technique and still the subject of active research. History[edit] In 1954, pioneering work on what is today known as artificial life was carried out by Nils Aall Barricelli using the very early computers.[1] In the 1960s and early 1970s, evolutionary algorithms became widely recognized as optimization methods.

In 1964, Lawrence J. Program representation[edit] [edit] Scientists create 'artificial evolution' for the first time. Scientists have made a significant step towards developing fully artificial life – for the first time, they demonstrated evolution in a simple chemistry set without DNA. In a way, the researchers showed that the principle of natural selection doesn’t only apply to the biological world. Using a simple a robotic ‘aid’, a team from the University of Glasgow managed to create an evolving chemical system. They used an open source robot based upon a cheap 3D printer to create and monitor droplets of oil. The droplets of oil were placed in water-filled Petri dishes, and each dropled had a slightly different mixture of 4 different chemical compounds.

Photographs of the droplet behaviour as a function of time (from left to right) for all the traits (given in a–i). The robot used a simple video camera to monitor, process and analyse the behaviour of 225 differently-composed droplets, identifying a number of distinct characteristics such as vibration or clustering. Genetic programming. In artificial intelligence, genetic programming (GP) is a technique whereby computer programs are encoded as a set of genes that are then modified (evolved) using an evolutionary algorithm (often a genetic algorithm - "GA"). The result is a computer program able to perform well in a predefined task.

Often confused to be a kind of genetic algorithm, GP can indeed be seen as an application of genetic algorithms to problems where each individual is a computer program. The methods used to encode a computer program in an artificial chromosome and to evaluate its fitness with respect to the predefined task are central in the GP technique and still the subject of active research.

History[edit] In 1954, pioneering work on what is today known as artificial life was carried out by Nils Aall Barricelli using the very early computers.[1] In the 1960s and early 1970s, evolutionary algorithms became widely recognized as optimization methods. In 1964, Lawrence J. Program representation[edit] [edit] Evolutionary computation. In computer science, evolutionary computation is a subfield of artificial intelligence (more particularly soft computing) that can be defined by the type of algorithms it is concerned with. These algorithms, called evolutionary algorithms, are based on adopting Darwinian principles, hence the name. Technically they belong to the family of trial and error problem solvers and can be considered global optimization methods with a metaheuristic or stochastic optimization character, distinguished by the use of a population of candidate solutions (rather than just iterating over one point in the search space).

The application of recombination and evolutionary strategies makes them less prone to get stuck in local optima than alternative methods.[1] Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. History[edit] Techniques[edit] Th. Evolutionary programming. Evolutionary programming is one of the four major evolutionary algorithm paradigms. It is similar to genetic programming, but the structure of the program to be optimized is fixed, while its numerical parameters are allowed to evolve. It was first used by Lawrence J. Fogel in the US in 1960 in order to use simulated evolution as a learning process aiming to generate artificial intelligence.

Fogel used finite-state machines as predictors and evolved them. Currently evolutionary programming is a wide evolutionary computing dialect with no fixed structure or (representation), in contrast with some of the other dialects. It is becoming harder to distinguish from evolutionary strategies. See also[edit] References[edit] External links[edit] Evolutionary programming. Evolutionary computation. Evolution strategy. History[edit] The 'evolution strategy' optimization technique was created in the early 1960s and developed further in the 1970s and later by Ingo Rechenberg, Hans-Paul Schwefel and their co-workers. Methods[edit] As far as real-valued search spaces are concerned, mutation is normally performed by adding a normally distributed random value to each vector component. The step size or mutation strength (i.e. the standard deviation of the normal distribution) is often governed by self-adaptation (see evolution window).

Individual step sizes for each coordinate or correlations between coordinates are either governed by self-adaptation or by covariance matrix adaptation (CMA-ES). The (environmental) selection in evolution strategies is deterministic and only based on the fitness rankings, not on the actual fitness values. Contemporary derivatives of evolution strategy often use a population of μ parents and also recombination as an additional operator, called (μ/ρ+, λ)-ES. See also[edit] Evolver (software) Portal:Evolutionary biology.

From Wikipedia, the free encyclopedia The Evolutionary Biology Portal The evolutionary history of life and origin of life are fields on ongoing geological and biological research. Although not necessary conditions for the acceptance of evolution by natural selection, the origin of life and its evolutionary history can nonetheless help shed light on evolutionary processes. The current scientific consensus is that the complex biochemistry that makes up life came from simpler chemical reactions, but it is unclear how this occurred. Not much is certain about the earliest developments in life, the structure of the first living things, or the identity and nature of any last universal common ancestor or ancestral gene pool. Consequently, there is no scientific consensus on how life began, but proposals include self-replicating molecules such as RNA, and the assembly of simple cells.

Evolutionary computation. Genetic algorithm. The 2006 NASA ST5 spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an evolved antenna. Methodology[edit] Optimization problems[edit] In a genetic algorithm, a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions. A typical genetic algorithm requires: a genetic representation of the solution domain,a fitness function to evaluate the solution domain. Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions and then to improve it through repetitive application of the mutation, crossover, inversion and selection operators.

Initialization[edit] The population size depends on the nature of the problem, but typically contains several hundreds or thousands of possible solutions. Selection[edit] Genetic operators[edit] Termination[edit] Variants[edit] Genetic programming. In artificial intelligence, genetic programming (GP) is a technique whereby computer programs are encoded as a set of genes that are then modified (evolved) using an evolutionary algorithm (often a genetic algorithm - "GA"). The result is a computer program able to perform well in a predefined task. Often confused to be a kind of genetic algorithm, GP can indeed be seen as an application of genetic algorithms to problems where each individual is a computer program. The methods used to encode a computer program in an artificial chromosome and to evaluate its fitness with respect to the predefined task are central in the GP technique and still the subject of active research. History[edit] In 1954, pioneering work on what is today known as artificial life was carried out by Nils Aall Barricelli using the very early computers.[1] In the 1960s and early 1970s, evolutionary algorithms became widely recognized as optimization methods.

In 1964, Lawrence J. Program representation[edit] [edit] Evolutionary Programming - Clever Algorithms: Nature-Inspired Programming Recipes. Evolutionary Programming, EP. Taxonomy Evolutionary Programming is a Global Optimization algorithm and is an instance of an Evolutionary Algorithm from the field of Evolutionary Computation. The approach is a sibling of other Evolutionary Algorithms such as the Genetic Algorithm, and Learning Classifier Systems.

It is sometimes confused with Genetic Programming given the similarity in name, and more recently it shows a strong functional similarity to Evolution Strategies. Inspiration Evolutionary Programming is inspired by the theory of evolution by means of natural selection. Specifically, the technique is inspired by macro-level or the species-level process of evolution (phenotype, hereditary, variation) and is not concerned with the genetic mechanisms of evolution (genome, chromosomes, genes, alleles). Metaphor A population of a species reproduce, creating progeny with small phenotypical variation. Strategy Procedure Pseudocode for Evolutionary Programming. Heuristics Code Listing References. Q1.2 - HHGT Evolutionary Computation. Introduction EVOLUTIONARY PROGRAMMING, originally conceived by Lawrence J.

Fogel in 1960, is a stochastic OPTIMIZATION strategy similar to GENETIC ALGORITHMs, but instead places emphasis on the behavioral linkage between PARENTs and their OFFSPRING, rather than seeking to emulate specific GENETIC OPERATORs as observed in nature. Evolutionary programming is similar to EVOLUTION STRATEGIEs, although the two approaches developed independently (see below). Like both ES and GAs, EP is a useful method of optimization when other techniques such as gradient descent or direct, analytical discovery are not possible. Combinatoric and real-valued FUNCTION OPTIMIZATION in which the optimization surface or FITNESS landscape is "rugged", possessing many locally optimal solutions, are well suited for evolutionary programming. History In 1992, the First Annual Conference on evolutionary programming was held in La Jolla, CA. The Process (1) Choose an initial POPULATION of trial solutions at random. EP and ES.