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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, causal inference, etc.
In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators.
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 computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing.
Swarm intelligence (SI) is 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.
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems.
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.
ParadisEO is a white-box object-oriented framework dedicated to the flexible design of metaheuristics. It uses EO, a template-based, ANSI-C++ compliant computation library. ParadisEO is portable across both Windows system and sequential platforms. ParadisEO is distributed under the CeCill license and can be used under several environments.
In mathematical optimization, the firefly algorithm is a metaheuristic proposed by Xin-She Yang and inspired by the flashing behavior of fireflies.
Fred Glover is Chief Scientific Officer of Entanglement, Inc., USA, in charge of algorithmic design and strategic planning for applications of combinatorial optimization in quantum computing. He also holds the title of Distinguished University Professor, Emeritus, at the University of Colorado, Boulder, associated with the College of Engineering and Applied Science and the Leeds School of Business. He is known for his innovations in the area of metaheuristics including the computer-based optimization methodology of Tabu search, an adaptive memory programming algorithm for mathematical optimization, and the associated evolutionary Scatter Search and Path Relinking algorithms.
In operations research, cuckoo search is an optimization algorithm developed by Xin-She Yang and Suash Deb in 2009. It was inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of host birds of other species. Some host birds can engage direct conflict with the intruding cuckoos. For example, if a host bird discovers the eggs are not their own, it will either throw these alien eggs away or simply abandon its nest and build a new nest elsewhere. Some cuckoo species such as the New World brood-parasitic Tapera have evolved in such a way that female parasitic cuckoos are often very specialized in the mimicry in colors and pattern of the eggs of a few chosen host species. Cuckoo search idealized such breeding behavior, and thus can be applied for various optimization problems. It has been shown that cuckoo search is a special case of the well-known -evolution strategy.
In numerical optimization, meta-optimization is the use of one optimization method to tune another optimization method. Meta-optimization is reported to have been used as early as in the late 1970s by Mercer and Sampson for finding optimal parameter settings of a genetic algorithm.
The Bat algorithm is a metaheuristic algorithm for global optimization. It was inspired by the echolocation behaviour of microbats, with varying pulse rates of emission and loudness. The Bat algorithm was developed by Xin-She Yang in 2010.
In computer science, imperialist competitive algorithms are a type of computational method used to solve optimization problems of different types. Like most of the methods in the area of evolutionary computation, ICA does not need the gradient of the function in its optimization process. From a specific point of view, ICA can be thought of as the social counterpart of genetic algorithms (GAs). ICA is the mathematical model and the computer simulation of human social evolution, while GAs are based on the biological evolution of species.
Enrique Alba is a professor of computer science at the University of Málaga, Spain.
Derivative-free optimization, is a discipline in mathematical optimization that does not use derivative information in the classical sense to find optimal solutions: Sometimes information about the derivative of the objective function f is unavailable, unreliable or impractical to obtain. For example, f might be non-smooth, or time-consuming to evaluate, or in some way noisy, so that methods that rely on derivatives or approximate them via finite differences are of little use. The problem to find optimal points in such situations is referred to as derivative-free optimization, algorithms that do not use derivatives or finite differences are called derivative-free algorithms.
Adaptive dimensional search algorithms differ from nature-inspired metaheuristic techniques in the sense that they do not use any metaphor as an underlying principle for implementation. Rather, they utilize a simple, performance-oriented methodology based on the update of the search dimensionality ratio (SDR) parameter at each iteration.
This is a chronological table of metaheuristic algorithms that only contains fundamental algorithms. Hybrid algorithms and multi-objective algorithms are not listed in the table below.
References
- ↑ "Dr Xin-She Yang | Middlesex University London". www.mdx.ac.uk. Retrieved 16 July 2023.
- ↑ "Oxford University Gazette". Archived from the original on 28 September 2012. Retrieved 4 January 2011.
- ↑ "SEA 2011 - 10th International Symposium on Experimental Algorithms". Rebennack.net. Retrieved 14 May 2013.
- ↑ "BIOMA 2012". Bioma.ijs.si. Retrieved 14 May 2013.
- ↑ "Highly Cited Researchers".
- ↑ Yang, X.-S. (2008). Nature-Inspired Metaheuristic Algorithms. Luniver Press.
- ↑ Yang, X.-S.; Deb, S. (2009). Cuckoo search via Lévy flights, in: World Congress on Nature & Biologically Inspired Computing (NaBIC 2009). IEEE Publication, USA. pp. 210–214.
- ↑ "Novel 'cuckoo search algorithm' beats particle swarm optimization in engineering design". Sciencedaily.com. 28 May 2010. Retrieved 14 May 2013.
- ↑ Tue, 06/01/2010 - 12:07pm. "Novel 'Cuckoo Search Algorithm' Beats Particle Swarm Optimization". Scientificcomputing.com. Archived from the original on 11 March 2012. Retrieved 14 May 2013.
- ↑ Yang X.-S., A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65–74 (2010)
