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Memetic algorithms
- 2. Supervised by Dr. Fawzia R. Mosul University Computer Sciences Department Master Student 2018 - 2019 Mohammed Al-Kazal ADVANCE ARTIFICIAL INTELLIGENCE
- 3. Overview Philosophy Behind Memetics Genetic Algorithm – Intuition and Structure Memetic learning • meme Memetic Algorithms • First generation MA • Second generation MA • Third generation MA Solution of N Queens Problem with MA Conclusion
- 5. BiologicalEvolution • Use and Disuse • Inheritance of acquired characteristics Charles Robert Darwin 1809 –1882 • Natural Selection • Survival of The Fittest • Origin of New Species Jean-Baptiste Lamarck 1744 –1829
- 6. Genes and biological Evolution A gene is a unit of biological information transferred from one generation to another. Genes determine our physical traits, what you look like, what you inherit from either one of your parents.
- 7. Genetic Algorithm solves (typically optimization) problems by combining features of complete solutions to create new populations of solutions. applicable when it is hard or unreasonable to try to completely identify a sub problem hierarchical structure or to approach the problem via an exact approach.
- 8. WhyGA Range of Problem PerformanceofMethod Problem tailored method Pure Genetic Algorithm Hill Clamping
- 9. GeneticAlgorithm start Initialize population Selection Cross over Mutation Evaluation End Stop condition No Yes NewGeneration Return optimal solution
- 10. Cross over
- 11. Mutatio n
- 12. Disadvantages&weaknessof GA • GAs are not ‘silver bullets’ for solving problems (Global Optimization Algorithm). • Must be able to assess the quality of each attempt at a solution. can’t crack Pretty Good Privacy with a GA. • Computationally expensive. some problems require many days or weeks to run. often still faster than brute force, however. • Blind, undirected search. difficult to direct a GA towards optimal solution area if known. • Can be sensitive to initial parameters. parameters such as mutation can significantly influence the search. • Stochastic process. not guaranteed to find an optimal solution, just highly likely to.
- 13. Range of Problem PerformanceofMethod Problem tailored method Pure Genetic Algorithm Time
- 14. But what we do in all our lives ? Is this the only way of evolution ?
- 15. Memetic Learning R. Dawkins, The Selfish Gene “When you plant a fertile meme in my mind you literally parasitize my brain, turning it into a vehicle for the meme's propagation in just the way that a virus may parasitize the genetic mechanism of a host cell.”
- 17. Meme “the basic unit of cultural transmission, or imitation” Richard Dawkins “an element of culture that may be considered to be passed on by non- genetic means” English Oxford Dictionary
- 18. ExamplesofMeme Fashion Latest trends are ideas of fashion designers Science Scientists sharing their thoughts Literature Novel, poetry Music Even birds are found to imitate songs of other birds!!!
- 19. Genes and Memes, where they are similar Genes propagate biologically from chromosome to chromosome Memes propagate from brain to brain via imitation Survival of fittest in meme Concept of Good is survived though no scientific evidence is present
- 20. Genes and Memes, where they different Genes are pre-decided Genes are static through generations, memes can be changed! Memes allow improvement After learning language, we contribute to it through literature New heuristics to 8-puzzle problem solved in class We use this property to improve genetic algorithms
- 21. Memeticlearning the idea of memetic learning. Memetic learning is directly inspired by Dawkins notion of the acquisition of memes through imitation. Memetic learning is based on imitation—people learn, not only from their own direct experiences with the world, but also by following patterns, models, or examples they have observed. This imitation is an active process by which people acquire the components of cultural knowledge. This process provides a rich metaphor on which several related machine-learning methods can be based. These methods will be known collectively as
- 22. Memetic algorithms (MAs) now represent one of the recent growing areas of research in evolutionary computation. The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Cultural algorithms, Hybrid Evolutionary Algorithms, Baldwinian evolutionary algorithms (EAs), Lamarckian EAs, or genetic local search. Memeticalgorithms (MAs) proposed by Moscato (1989)
- 23. Memeticalgorithms (MAs) Moscato recognized that there are at least two great differences between biological evolution and cultural evolution: individuals cannot choose their own genes whereas memes can be acquired intentionally. Individuals may modify and improve upon the memes that they acquire, whereas they
- 24. A Compact “Memetic” Algorithm by Merz (2003) Invariantsand Decorations
- 27. So.. What Are Memetic Algorithms? MAs, one of the key methodologies behind successful discrete/continuous optimization, are: a carefully orchestrated interplay between (stochastic) global search and (stochastic)local search algorithms An MA is an EA that includes one or more local search phases within its evolutionary cycle
- 28. MemeticAlgorithm NewGeneration Selection Cross over Evaluation End Stop condition No Yes star t Initialize population Mutation local search Return optimal solution local search local search Evaluate and Re-Generate population
- 32. Memetic Learning A population of individuals with random alleles for each meme is constructed and tested on the task. Individuals observe their own overall fitness values and those of the other individuals in the population. Further they observe the partial fitness values of the partial candidate solutions that they are able to identify, both for themselves and for the others. They then replace their memes for those portions of the solution for which they have low fitness values, using imitation.
- 33. DesignIssuesforMAs We mast balance between the genetic and the local search parts of memetic algorithms .This leads naturally to questions such as the following: Where and when should local search be applied within the evolutionary cycle. Which individuals in the population should be improved by local search, and how should they be chosen. How much computational effort should be allocated to each local search. How can the genetic operators best be integrated with local search in order to achieve a synergistic effect.
- 34. “What is the best tradeoff between local search and the global search provided by evolution” DesignIssuesforMA How often should individual learning be applied. On which solutions should individual learning be used. How long should individual learning be run. What individual learning method or meme should be used for a particular problem or individual.
- 35. Multi-meme, Hyper-heuristic and Meta-Lamarckian MA. MASecond Generation MA considering Multiple Individual Learning methods within an evolutionary system. In second generation MA
- 36. In Multi-meme MA, the memetic material is encoded as part of the genotype. The decoded meme of each respective individual/chromosome is used to perform a local refinement. The memetic material is transmitted through a simple inheritance mechanism from parent to offspring(s). In Multi-meme MA the pool of candidate memes considered will compete, based on their past merits in generating local improvements through a reward mechanism, deciding on which meme to be selected to proceed for future local refinements. Memes with a higher reward have a greater chance of being replicated or copied. MASecondGeneration
- 37. 3rd generation MA utilizes a rule-based local search to supplement candidate solutions within the evolutionary system, thus capturing regularly repeated features or patterns in the problem space. There'dGenerationMA In 2nd generation MA we assumes that the memes to be used are known a priori. individual can select his meme Individual can have more than one meme
- 39. 1 2 3 4 5 6 7 8 2 7 3 4 6 5 8 1 1 2 3 4 5 6 7 8 3 5 2 8 1 7 4 6 Fitness = 22 Fitness = 28 NQueensProblem
- 40. NQueensProblemGA Cross over every two parents to random point Sorting the generation from the highest value of fitness function to the lower one Duplicate the higher half of the generation and neglect the lowest Start Create initial population random Chromosome fond in solation Copy chromosome and all his symmetric to solution Yes YesNo Calculates fitness any = maximum fitness Q = 1 No Q = Q + 1 Mutation random gene in random children's Q < generation number Print solution array Yes No NewGeneration YesNo Yes No No
- 41. One Point cross over Ex: random cross over point =3 CrossoverinGA
- 42. MutationinGA Select a number of individuals randomly from the generation for mutation Mutation is Random change to break typical
- 43. Mutation random gene in random children's NQueensProblemMA Cross over every two parents to random point Sorting by fitness & Duplicate the higher half Start Create initial population random Chromosome fond in solation Copy chromosome and all his symmetric to solution Yes YesNo Calculates fitness any = maximum fitness Q = 1 No Q = Q + 1 Q < generation number Print solution array Yes YesNo Yes No No Sorting by fitness Local Search (meme)
- 44. • Select a subset M of random individuals from the generation. • For each individual in M Apply local search (generate neighbor) for t times. • Add the new individual to the generation . LocalsearchinMA
- 45. LocalsearchinMA 2 4 5 8 1 7 3 6 Top ……… ……… ……… …… …… Random point 1 If (Fitness before < Fitness after ) copy Generation Subset M Then Update individual t 2 4 1 8 5 7 3 6 Random point 2
- 46. • Select a subset M of random individuals from the generation. • For each individual in M Apply local search to t times with Meme . • Add the new individual to the generation . LocalsearchMemeticlearning
- 47. Memeticlearning 2 4 4 8 1 7 3 6 Top ……… ……… ……… …… …… Meme (best solution in generation) Random point 3 5 2 8 1 7 4 6 If (Fitness before < Fitness after ) copy Generation Subset M Then Update individual t 2 5 3 8 1 7 4 6
- 48. • Split generation into N groups of random individuals from generation. • For each group n: • For each individual in the group Apply local search to tn times with meme n. • Add the new individual to the generation. LocalsearchMultiMemeticlearning
- 49. 2 5 4 8 1 7 3 6 Meme 1 Meme2 Meme3 …. ……… ……… ……… …… …… Meme n (one of best solution in generation) Random point 3 5 2 8 1 7 4 6 If (Fitness before < Fitness after ) copy Generation Subset 1 Then Update individual tn …… …… Subset n …… ….... …… 2 5 3 8 1 7 4 6 LocalsearchMultiMemeticlearning
- 50. Conclusion A genetic algorithm promises convergence but not optimality. But we are assured of exponential convergence, possibly at different optimal chromosomes. We can reach Optimal solution from Memetic Learning (Genetic Algorithm with Local Search). In MA we mast balance between the genetic and the local search parts of memetic algorithms . It enables us to deal with problems of high change in performance and ambiguous problems
- 51. Performance Range of Problem PerformanceofMethod Problem tailored method Pure Genetic Algorithm Memetic Algorithm
- 52. References Ahmad, R., Jamaluddin, H., Hussain, M.A.: Application of memetic algorithm in modelling discrete-time multivariable dynamics systems. Mech. Syst. Signal Process. 22, 1595–1609 (2008) R. Dawkins, “The Selfish Gene – new edition”, Oxford University Press, 1989 pp 189-201 David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1st edition, Addison-Wesley Longman Publishing Co., 1989 pp 170- 174 B. Freisleben and P. Merz, New Genetic Local Search Operators for the Traveling Salesman Problem. In H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors, Proceedings of the 4th Conference on Parallel Problem Solving from Nature - PPSN IV, pages 890--900. Springer, 1996 S. Lin and B. W. Kemighan, An effective heuristic algorithm for the Traveling Salesman problem, Operation Research 21 (1973) 498-516 7.) Moscato, P. (1989). On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts:Towards Memetic Algorithms. Technical Report Caltech Concurrent Computation Program,Report. 826, California Institute of Technology, Pasadena, California, USA. Singh, S. P. and Sutton, R. S. (1996). Reinforcement learning with replacing eligibility traces.Machine Learning, 22:123–158. Sutton, R. S. and Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press.Yao, X. (1999). Evolutionary computation comes of age. Cognitive Systems Research, 1:59–64.
- 54. الغزال محمد