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In artificial intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs.
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.
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying").
Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. It is most commonly applied in artificial life, general game playing and evolutionary robotics. The main benefit is that neuroevolution can be applied more widely than supervised learning algorithms, which require a syllabus of correct input-output pairs. In contrast, neuroevolution requires only a measure of a network's performance at a task. For example, the outcome of a game can be easily measured without providing labeled examples of desired strategies. Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use backpropagation with a fixed topology.
Karl Sims is a computer graphics artist and researcher, who is best known for using particle systems and artificial life in computer animation.
In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover. As well, it may have interfaces for selective evaluation. In short, a HBGA outsources the operations of a typical genetic algorithm to humans.
Evolutionary robotics is an embodied approach to Artificial Intelligence (AI) in which robots are automatically designed using Darwinian principles of natural selection. The design of a robot, or a subsystem of a robot such as a neural controller, is optimized against a behavioral goal. Usually, designs are evaluated in simulations as fabricating thousands or millions of designs and testing them in the real world is prohibitively expensive in terms of time, money, and safety.
In computer programming, genetic representation is a way of presenting solutions/individuals in evolutionary computation methods. The term encompasses both the concrete data structures and data types used to realize the genetic material of the candidate solutions in the form of a genome, and the relationships between search space and problem space. In the simplest case, the search space corresponds to the problem space. The choice of problem representation is tied to the choice of genetic operators, both of which have a decisive effect on the efficiency of the optimization. Genetic representation can encode appearance, behavior, physical qualities of individuals. Difference in genetic representations is one of the major criteria drawing a line between known classes of evolutionary computation.
A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem. It uses a suitable heuristic or local search technique to improve the quality of solutions generated by the EA and to reduce the likelihood of premature convergence.
Human-based computation (HBC), human-assisted computation, ubiquitous human computing or distributed thinking is a computer science technique in which a machine performs its function by outsourcing certain steps to humans, usually as microwork. This approach uses differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human–computer interaction. For computationally difficult tasks such as image recognition, human-based computation plays a central role in training Deep Learning-based Artificial Intelligence systems. In this case, human-based computation has been referred to as human-aided artificial intelligence.
Human-based evolutionary computation (HBEC) is a set of evolutionary computation techniques that rely on human innovation.
Evolutionary music is the audio counterpart to evolutionary art, whereby algorithmic music is created using an evolutionary algorithm. The process begins with a population of individuals which by some means or other produce audio, which is either initialized randomly or based on human-generated music. Then through the repeated application of computational steps analogous to biological selection, recombination and mutation the aim is for the produced audio to become more musical. Evolutionary sound synthesis is a related technique for generating sounds or synthesizer instruments. Evolutionary music is typically generated using an interactive evolutionary algorithm where the fitness function is the user or audience, as it is difficult to capture the aesthetic qualities of music computationally. However, research into automated measures of musical quality is also active. Evolutionary computation techniques have also been applied to harmonization and accompaniment tasks. The most commonly used evolutionary computation techniques are genetic algorithms and genetic programming.
3form Free Knowledge Exchange is one of the earliest examples of human-based computation and human-based genetic algorithm. It uses both human-based selection and three types of human-based innovation, in order to implement collaborative problem-solving between humans.
NEAT Particles is an interactive evolutionary computation program that enables users to evolve particle systems intended for use as special effects in video games or movie graphics. Rather than being hand-coded like typical particle systems, the behaviors of NEAT Particle effects are evolved by user preference. Therefore, non-programmer, non-artist users may evolve complex and unique special effects in real time. NEAT Particles is meant to augment and assist the time-consuming computer graphics content generation process. NEAT is short for Neuroevolution of Augmenting Topologies.
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 computer science, an evolving intelligent system is a fuzzy logic system which improves the own performance by evolving rules. The technique is known from machine learning, in which external patterns are learned by an algorithm. Fuzzy logic based machine learning works with neuro-fuzzy systems.
References
- ↑ Dawkins, R. (1986). The Blind Watchmaker. Longman.
- ↑ Takagi, H. (2001). "Interactive Evolutionary Computation: Fusion of the Capacities of EC Optimization and Human Evaluation" (PDF). Proceedings of the IEEE. 89 (9): 1275–1296. doi:10.1109/5.949485. hdl: 2324/1670053 .
- ↑ Kruse, J.; Connor, A.M. (2015). "Multi-agent evolutionary systems for the generation of complex virtual worlds". EAI Endorsed Transactions on Creative Technologies. 15 (5): 150099. arXiv: 1604.05792 . doi:10.4108/eai.20-10-2015.150099. S2CID 12670076.
- ↑ Herdy, M. (1997), Evolutionary Optimisation based on Subjective Selection – evolving blends of coffee. Proceedings 5th European Congress on Intelligent Techniques and Soft Computing (EUFIT’97); pp 2010-644.
- ↑
- Caldwell, C. and Johnston, V.S. (1991), Tracking a Criminal Suspect through "Face-Space" with a Genetic Algorithm, in Proceedings of the Fourth International Conference on Genetic Algorithm, Morgan Kaufmann Publisher, pp.416-421, July 1991
- ↑ Milani, A. (2004). "Online Genetic Algorithms" (PDF). International Journal of Information Theories and Applications. 11: 20–28.
- ↑ Sims, K. (1991). "Artificial Evolution for Computer Graphics". Computer Graphics. 25 (4): 319–328. CiteSeerX 10.1.1.226.7450 . doi:10.1145/127719.122752.
- ↑ Sims, K. (1991), Interactive Evolution of Dynamical Systems. First European Conference on Artificial Life, MIT Press
- ↑ Unemi, T. (2000). SBART 2.4: an IEC tool for creating 2D images, Movies and Collage, Proceedings of 2000 Genetic and Evolutionary Computational Conference workshop program, Las Vegas, Nevada, July 8, 2000, p.153
- ↑ Kosorukoff, A. (2001). "Human based genetic algorithm". 2001 IEEE International Conference on Systems, Man and Cybernetics. E-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236). Vol. 5. IEEE Transactions on Systems, Man, and Cybernetics. pp. 3464–3469. doi:10.1109/ICSMC.2001.972056. ISBN 978-0-7803-7087-6. S2CID 13839604.
- ↑ khan, Shahroz; Gunpinar, Erkan; Sener, Bakir (2019). "GenYacht: An interactive generative design system for computer-aided yacht hull design". Ocean Engineering. 191: 106462. doi:10.1016/j.oceaneng.2019.106462. S2CID 204150911.
- Banzhaf, W. (1997), Interactive Evolution, Entry C2.9, in: Handbook of Evolutionary Computation, Oxford University Press, ISBN 978-0750308953