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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 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, or select a heuristic that may provide a sufficiently good solution to an optimization 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 artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. The algorithms are typically modeled after the immune system's characteristics of learning and memory for use in problem-solving.
Kalyanmoy Deb is an Indian computer scientist. Deb is the Herman E. & Ruth J. Koenig Endowed Chair in Communication Systems in the Department of Electrical and Computing Engineering at Michigan State University. Deb is also a professor in the Department of Computer Science and Engineering and the Department of Mechanical Engineering at Michigan State University.
IEEE Transactions on Evolutionary Computation is a bimonthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. The editor-in-chief is Carlos A. Coello Coello (CINVESTAV). According to the Journal Citation Reports, the journal has a 2021 impact factor of 16.497.
Multi-objective optimization is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimization problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives.
Design Automation usually refers to electronic design automation, or Design Automation which is a Product Configurator. Extending Computer-Aided Design (CAD), automated design and Computer-Automated Design (CAutoD) are more concerned with a broader range of applications, such as automotive engineering, civil engineering, composite material design, control engineering, dynamic system identification and optimization, financial systems, industrial equipment, mechatronic systems, steel construction, structural optimisation, and the invention of novel systems.
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.
In applied mathematics, multimodal optimization deals with optimization tasks that involve finding all or most of the multiple solutions of a problem, as opposed to a single best solution. Evolutionary multimodal optimization is a branch of evolutionary computation, which is closely related to machine learning. Wong provides a short survey, wherein the chapter of Shir and the book of Preuss cover the topic in more detail.
Multi-swarm optimization is a variant of particle swarm optimization (PSO) based on the use of multiple sub-swarms instead of one (standard) swarm. The general approach in multi-swarm optimization is that each sub-swarm focuses on a specific region while a specific diversification method decides where and when to launch the sub-swarms. The multi-swarm framework is especially fitted for the optimization on multi-modal problems, where multiple (local) optima exist.
In applied mathematics, test functions, known as artificial landscapes, are useful to evaluate characteristics of optimization algorithms, such as:
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 Genetic and Evolutionary Computation Conference (GECCO) is the premier conference in the area of genetic and evolutionary computation. GECCO has been held every year since 1999, when it was first established as a recombination of the International Conference on Genetic Algorithms (ICGA) and the Annual Genetic Programming Conference (GP).
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.
Maurice Clerc is a French mathematician.
Adriana Lara López is a Mexican computer scientist whose research involves evolutionary computation, memetic algorithms, and multi-objective optimization. She is a professor in the school of physics and mathematics at the Instituto Politécnico Nacional in Mexico.
References
- ↑ "Dr. Carlos A. Coello Coello". Departamento de Computación - CINVESTAV. Retrieved 25 February 2020.
- ↑ "Carlos A. Coello Coello - Engineering and Technology History Wiki". ethw.org.