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Cooperative Coevolution
Cooperative Coevolution (CC) in the field of biological evolution is an evolutionary computation method. It divides a large problem into subcomponents, and solves them independently in order to solve the large problem. The subcomponents are also called species. The subcomponents are implemented as subpopulations and the only interaction between subpopulations is in the cooperative evaluation of each individual of the subpopulations. The general CC framework is nature inspired where the individuals of a particular group of species mate amongst themselves, however, mating in between different species is not feasible. The cooperative evaluation of each individual in a subpopulation is done by concatenating the current individual with the best individuals from the rest of the subpopulations as described by M. Potter.Mitchell A. Potter, Kenneth A. De JongCooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents Evolutionary Computation, v.8 n.1, 2000, p.1-29. The coo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Biological Evolution
Evolution is change in the heritable characteristics of biological populations over successive generations. These characteristics are the expressions of genes, which are passed on from parent to offspring during reproduction. Variation tends to exist within any given population as a result of genetic mutation and recombination. Evolution occurs when evolutionary processes such as natural selection (including sexual selection) and genetic drift act on this variation, resulting in certain characteristics becoming more common or more rare within a population. The evolutionary pressures that determine whether a characteristic is common or rare within a population constantly change, resulting in a change in heritable characteristics arising over successive generations. It is this process of evolution that has given rise to biodiversity at every level of biological organisation, including the levels of species, individual organisms, and molecules. The theory of evolution by na ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Evolutionary Computation
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection) and mutation. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm. Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computational Problem
In theoretical computer science, a computational problem is a problem that may be solved by an algorithm. For example, the problem of factoring :"Given a positive integer ''n'', find a nontrivial prime factor of ''n''." is a computational problem. A computational problem can be viewed as a set of ''instances'' or ''cases'' together with a, possibly empty, set of ''solutions'' for every instance/case. For example, in the factoring problem, the instances are the integers ''n'', and solutions are prime numbers ''p'' that are the nontrivial prime factors of ''n''. Computational problems are one of the main objects of study in theoretical computer science. The field of computational complexity theory attempts to determine the amount of resources ( computational complexity) solving a given problem will require and explain why some problems are intractable or undecidable. Computational problems belong to complexity classes that define broadly the resources (e.g. time, space/memory, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Species
In biology, a species is the basic unit of Taxonomy (biology), classification and a taxonomic rank of an organism, as well as a unit of biodiversity. A species is often defined as the largest group of organisms in which any two individuals of the appropriate sexes or mating types can reproduction, produce Fertility, fertile offspring, typically by sexual reproduction. Other ways of defining species include their karyotype, DNA sequence, morphology (biology), morphology, behaviour or ecological niche. In addition, paleontologists use the concept of the chronospecies since fossil reproduction cannot be examined. The most recent rigorous estimate for the total number of species of eukaryotes is between 8 and 8.7 million. However, only about 14% of these had been described by 2011. All species (except viruses) are given a binomial nomenclature, two-part name, a "binomial". The first part of a binomial is the genus to which the species belongs. The second part is called the specifi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mating
In biology, mating is the pairing of either opposite- sex or hermaphroditic organisms for the purposes of sexual reproduction. ''Fertilization'' is the fusion of two gametes. '' Copulation'' is the union of the sex organs of two sexually reproducing animals for insemination and subsequent internal fertilization. Mating may also lead to external fertilization, as seen in amphibians, fishes and plants. For most species, mating is between two individuals of opposite sexes. However, for some hermaphroditic species, copulation is not required because the parent organism is capable of self-fertilization ( autogamy); for example, banana slugs. The term ''mating'' is also applied to related processes in bacteria, archaea and viruses. Mating in these cases involves the pairing of individuals, accompanied by the pairing of their homologous chromosomes and then exchange of genomic information leading to formation of recombinant progeny (see mating systems). Animals For anim ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Concatenating
In formal language theory and computer programming, string concatenation is the operation of joining character strings end-to-end. For example, the concatenation of "snow" and "ball" is "snowball". In certain formalisations of concatenation theory, also called string theory, string concatenation is a primitive notion. Syntax In many programming languages, string concatenation is a binary infix operator. The + (plus) operator is often overloaded to denote concatenation for string arguments: "Hello, " + "World" has the value "Hello, World". In other languages there is a separate operator, particularly to specify implicit type conversion to string, as opposed to more complicated behavior for generic plus. Examples include . in Edinburgh IMP, Perl, and PHP, .. in Lua, and & in Ada, AppleScript, and Visual Basic. Other syntax exists, like , , in PL/I and Oracle Database SQL. In a few languages, notably C, C++, and Python, there is string literal concatenation, mean ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Coevolution
In biology, coevolution occurs when two or more species reciprocally affect each other's evolution through the process of natural selection. The term sometimes is used for two traits in the same species affecting each other's evolution, as well as gene-culture coevolution. Charles Darwin mentioned evolutionary interactions between flowering plants and insects in ''On the Origin of Species'' (1859). Although he did not use the word coevolution, he suggested how plants and insects could evolve through reciprocal evolutionary changes. Naturalists in the late 1800s studied other examples of how interactions among species could result in reciprocal evolutionary change. Beginning in the 1940s, plant pathologists developed breeding programs that were examples of human-induced coevolution. Development of new crop plant varieties that were resistant to some diseases favored rapid evolution in pathogen populations to overcome those plant defenses. That, in turn, required the development o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pedestrian
A pedestrian is a person traveling on foot, whether walking or running. In modern times, the term usually refers to someone walking on a road or pavement, but this was not the case historically. The meaning of pedestrian is displayed with the morphemes ''ped-'' ('foot') and ''-ian'' ('characteristic of'). This word is derived from the Latin term ''pedester'' ('going on foot') and was first used (in English language) during the 18th century. It was originally used, and can still be used today, as an adjective meaning plain or dull. However, in this article it takes on its noun form and refers to someone who walks. The word pedestrian may have been used in middle French in the Recueil des Croniques et Anchiennes Istories de la Grant Bretaigne, à présent nommé Engleterre. In California the definition of a pedestrian has been broadened to include anyone on any human powered vehicle that is not a bicycle, as well as people operating self-propelled wheelchairs by reason of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neural Network
A neural network is a network or neural circuit, circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, used for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled in artificial neural networks as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1. These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning re ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Constructive Cooperative Coevolution
The constructive cooperative coevolutionary algorithm (also called C3) is a global optimisation algorithm in artificial intelligence based on the multi-start architecture of the greedy randomized adaptive search procedure (GRASP). It incorporates the existing cooperative coevolutionary algorithm (CC). The considered problem is decomposed into subproblems. These subproblems are optimised separately while exchanging information in order to solve the complete problem. An optimisation algorithm, usually but not necessarily an evolutionary algorithm, is embedded in C3 for optimising those subproblems. The nature of the embedded optimisation algorithm determines whether C3's behaviour is deterministic or stochastic. The C3 optimisation algorithm was originally designed for simulation-based optimisation but it can be used for global optimisation problems in general.Glorieux E., Svensson B., Danielsson F., Lennartson B."Constructive cooperative coevolution for large-scale global optimisat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Algorithms
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc. Methodology Optimization problems In a genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possibl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Differential Evolution
In evolutionary computation, differential evolution (DE) is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such methods are commonly known as metaheuristics as they make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. However, metaheuristics such as DE do not guarantee an optimal solution is ever found. DE is used for multidimensional real-valued functions but does not use the gradient of the problem being optimized, which means DE does not require the optimization problem to be differentiable, as is required by classic optimization methods such as gradient descent and quasi-newton methods. DE can therefore also be used on optimization problems that are not even continuous, are noisy, change over time, etc. DE optimizes a problem by maintaining a population of candidate solutions and creating new candidate solutions by combi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
