Auto Topic: genetic
auto_genetic | topic
Coverage Score
1
Mentioned Chunks
40
Mentioned Docs
1
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
genetic
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: genetic | CO_OCCURS | Logical Agents | 8 |
| Auto Topic: genetic | CO_OCCURS | Propositional Logic | 6 |
| Auto Topic: genetic | CO_OCCURS | State-Space Search | 5 |
| Auto Topic: genetic | CO_OCCURS | Inference | 5 |
| Auto Topic: conference | CO_OCCURS | Auto Topic: genetic | 5 |
| Auto Topic: genetic | CO_OCCURS | Auto Topic: mitchell | 4 |
| Auto Topic: genetic | CO_OCCURS | Constraint Satisfaction Problem | 4 |
| Auto Topic: genetic | CO_OCCURS | Task Environment | 3 |
| Auto Topic: genetic | CO_OCCURS | Informed Search | 3 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: genetic | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.65 | 6 | ep neural networks (Miikkulainen et al., 2019). The field of genetic programming is a subfield of genetic algorithms in which the rep- resentations are programs rather than bit strings. The programs are represented in the form of syntax trees, either in a standard programming langu ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | sor to the development of genetic algorithms. In the 1950s, several statisticians, including Box (1957) and Friedman (1959), used evolutionary techniques for optimization problems, but it wasn’t until Rechenberg (1965) introducedevolution strategies to solve op- timization proble ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | ... 2411 24415124 32748552 24752411 32752124 24415411 24748552 32752411 24415124 32543213 32252124 24752411 32748152 24415417 Figure 4.6 A genetic algorithm, illustrated for digit strings representing 8-queens states. The initial population in (a) is ranked by a fitness function in (b ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | LENGTH (parent1) c ←random number from 1 to n return APPEND (SUBSTRING (parent1, 1,c), SUBSTRING (parent2, c + 1, n)) Figure 4.8 A genetic algorithm. Within the function, population is an ordered list of indi- viduals, weights is a list of corresponding fitness values for each ind ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | Ridley (2004), and Carroll (2007) provide general background on evolution. Most comparisons of genetic algorithms to other approaches (especially stochastic hill climbing) have found that the genetic algorithms are slower to converge (O’Reilly and Op- pacher, 1994; Mitchell et al ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | comes less diverse, and smaller steps are typical. Figure 4.8 describes an algorithm that implements all these steps. Genetic algorithms are similar to stochastic beam search, but with the addition of the crossover operation. This is advantageous if there are blocks that perform ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... , 31, 38–43. Kowalski, R. and Sergot, M. (1986). A logic-based calculus of events. New Generation Computing, 4, 67– 95. Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Se- lection. MIT Press. Koza, J. R. (1994). Genetic Programming II: ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... , 778, 882 generator, 1082 generator network (in GANs), 831 Genesereth, M. R., 79, 162, 225, 296, 297, 316, 321, 329, 330, 1096, 1104, 1113 GENETIC -ALGORITHM , 137 genetic algorithm, 134, 133–137, 161–162 genetic programming, 39, 134, 161 Gene Ontology Consortium, The., 358, 109 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | alphabet ACGT. In evolution strategies, an individual is a sequence of real numbers, and inEvolution strategies genetic programming an individual is a computer program.Genetic programming • The mixing number, ρ, which is the number of parents that come together to form offspring. ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... hings that are easy to learn need not reside there (Morgan and Griffiths, 2015). Section 4.2 Local Search in Continuous Spaces 137 function GENETIC -ALGORITHM (population, fitness) returns an individual repeat weights ← WEIGHTED -B Y(population, fitness) population2 ←empty list for ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... cover evolutionary algorithms; articles are also found inComplex Systems, Adaptive Behavior, and Artificial Life. The main conference is the Genetic and Evolutionary Com- 162 Chapter 4 Search in Complex Environments putation Conference (GECCO). Good overview texts on genetic algor ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | used to analyze genetic inheritance in family trees (so-called pedigree analysis) are in fact a special form Pedigree analysis 8 The title of the original version of the article was “Pearl for swine.” 474 Chapter 13 Probabilistic Reasoning of Bayesian networks. Exact inference al ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... l cards for model reporting. Proc. of the Conference on Fairness, Accountability, and Transparency. Mitchell, M. (1996). An Introduction to Genetic Algo- rithms. MIT Press. Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux. Mitche ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | LGORITHM , 137 genetic algorithm, 134, 133–137, 161–162 genetic programming, 39, 134, 161 Gene Ontology Consortium, The., 358, 1096 Gent, I., 191, 1096 Geometry Theorem Prover, 37 Georgeson, M., 1031, 1089 Georgiev, P., 48, 225, 873,1115 Gerbault, F., 799, 1096 Gerkin, R. C., 102 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... 1950 article “Com- 36 Chapter 1 Introduction puting Machinery and Intelligence.” Therein, he introduced the Turing test, machine learning, genetic algorithms, and reinforcement learning. He dealt with many of the objections raised to the possibility of AI, as described in Chapter ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... illusion of unlimited computational power was not confined to problem-solving pro- grams. Early experiments in machine evolution (now called genetic programming) (Fried- Machine evolution berg, 1958; Friedberg et al. , 1959) were based on the undoubtedly correct belief that by mak ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... s, the popu- lation becomes less diverse, and smaller steps are typical. Figure 4.8 describes an algorithm that implements all these steps. Genetic algorithms are similar to stochastic beam search, but with the addition of |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... cessors are generated from multiple individuals rather than just one. The actual mechanisms of evolution are, however, far richer than most genetic algorithms allow. For example, mutations can involve reversals, du- plications, and movement of large chunks of DNA; some viruses bo ... |