1. Organic Evolution and Problem Solving Je-Gun Joung

2. 1.2 Evolutionary Algorithms and Artificial Intelligence t 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. t Some researchers in AI propose to orientate toward imitation of the much more restricted capabilities of less complex animals

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

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

5. Learning Characteristic (2) t 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 t Learning by induction: Inductive inferences  Learning from examples (concept acquisition)  Learning by observation and discovery (descriptive generalization, unsupervised learning)

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

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

8. Automatic Programming t Finding a program which calculate a certain input- output function t 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) t 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. t The approach measured quality of the program by combining the binary feedback information

10. Optimization t Bremermann’s work was more oriented towards optimization in 1962. t Multiple mutations are necessary to overcome “points of stagnation” t Optimal mutation probability-1/l

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

12. Evolutionary Operation (E VOP ) t EVOP approach as presented by Box in 1957. t This method emphasized on the natural model of the organic evolution by performing a mutation-selection process. t ( ) -strategy (where or, the so-called 2 2 and 2 3 factorial design method

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

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