1 / 14

Organic Evolution and Problem Solving

Organic Evolution and Problem Solving. Je-Gun Joung. 1.2 Evolutionary Algorithms and Artificial Intelligence. A definition of artificial Intelligence by Rich Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better.

abrial
Download Presentation

Organic Evolution and Problem Solving

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Organic Evolution and Problem Solving Je-Gun Joung

  2. 1.2 Evolutionary Algorithms and Artificial Intelligence • A definition of artificial Intelligence by Rich Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better. • Some researchers in AI propose to orientate toward imitation of the much more restricted capabilities of less complex animals

  3. Representation • The symbolic period of AI can be dated period from 1962 until 1975. • A knowledge-intensive period from 1976 until 1988. • Currently, the field of AI is starting to spread research into a variety of directions • Subsymbolic period of AI dates from 1950 until 1965. • Evolutionary Algorithms make use of a subsymbolic representation of knowledge encoded in the genotypes of individuals

  4. Learning Characteristic • Rote learning: No inference processes take place. Instead, direct implantation of knowledge is performed. • Learning by instruction: This term denotes knowledge acquisition from a teacher or from an organized source and integration with existing knowledge. • Learning by deduction: Deductive, truth-preserving inferences and memorization of useful conclusions are summarized by this term

  5. Learning Characteristic (2) • Learning by analogy: The transformation of existing knowledge that bears strong similarity to the desired new concept into a form effectively useful in the new situation • Learning by induction: Inductive inferences • Learning from examples (concept acquisition) • Learning by observation and discovery (descriptive generalization, unsupervised learning)

  6. Artificial Life • Artificial Life research concentrates on computer simulations of simple hypothetical life forms • The problem how to make their behavior adaptive. • Self-organizing properties emerging from local interactions within a large number of simple basic agents are investigated. • Analogies to natural systems can be drawn on a variety of different levels. • In many cases the agents are equipped with internal rules of strategies determining their behavior

  7. 1.4 Early Approaches • Attempts to model natural evolution as a method for searching for good solutions of problems defined on vast search spaces. • Very restricted computer power was available at that time • Automatic programming, sequence prediction, numerical optimization, and optimal control

  8. Automatic Programming • Finding a program which calculate a certain input-output function • An attempt towards evolving computer programs as performed by Friedberg et al. In 1958 • Binary encoded • Modification by instruction interchange and random changes of instructions • “Success number” for instructions • The mutation rate depended on the success numbers

  9. Automatic Programming (2) • Selective pressure • To test different programs created by random instruction changes and instruction interchanges • To choose the best of the new programs as the next starting point. • The approach measured quality of the program by combining the binary feedback information

  10. Optimization • Bremermann’s work was more oriented towards optimization in 1962. • Multiple mutations are necessary to overcome “points of stagnation” • Optimal mutation probability-1/l

  11. Evolutionary Programming • Forgel: 1964 • A more complicated application domain • A sequence prediction problem ( finite-state-machine: FSM) • Population-based algorithm

  12. Evolutionary Operation (EVOP) • EVOP approach as presented by Box in 1957. • This method emphasized on the natural model of the organic evolution by performing a mutation-selection process. • ( ) -strategy (where or , the so-called 22 and 23 factorial design method

  13. 1.5 Summary • The basic process of transcription and translation , the genetic code and the hierarchical structure of genetic information • In connection to meiotic heredity, the crossover mechanism and the various forms of mutation • Evolution processes on the lower level of biological macromolecules

  14. Summary • Evolutionary algorithms are inductive learning algorithms that can serve as a powerful search method in many fields of AI research. • Three examples of global optimization problems • Computational complexity of global optimization problems • The early approaches

More Related