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Memetic Algorithms

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This was an invited talk at a GECCO 2009 workshop. This slide should be read in conjunction with the other slideshow on MAs

This was an invited talk at a GECCO 2009 workshop. This slide should be read in conjunction with the other slideshow on MAs

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  • 1. Memetic Algorithms Dr. N. Krasnogor Interdisciplinary Optimisation Laboratory Automated Scheduling, Optimisation and Planning Research Group School of Computer Science & Information Technology University of Nottingham www.cs.nott.ac.uk/~nxk Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 2. Based on: N. Krasnogor. Handbook of Natural Computation, chapter Memetic Algorithms, page (to appear). Natural Computing. Springer Berlin / Heidelberg, 2009. M. Tabacman, J. Bacardit, I. Loiseau, and N. Krasnogor. Learning classifier systems in optimisation problems: a case study on fractal travelling salesman problems. In Proceedings of the International Workshop on Learning Classifier Systems, volume (to appear) of Lecture Notes in Computer Science. Springer, 2008 W.E. Hart, N. Krasnogor, and J.E. Smith, editors. Recent advances in memetic algorithms, volume 166 of Studies in Fuzzyness and Soft Computing. Springer Berlin Heidelberg New York, 2004. ISBN 3-540-22904-3. N. Krasnogor and J.E. Smith. A tutorial for competent memetic algorithms: model, taxonomy and design issues. IEEE Transactions on Evolutionary Computation, 9(5):474- 488, 2005 J. Bacardit and N. Krasnogor. Performance and efficiency of memetic pittsburgh learning classifier systems. Evolutionary Computation, 17(3):(to appear), 2009. Q.H. Quang, Y.S. Ong, M.H. Lim, and N. Krasnogor. Adaptive cellular memetic algorithm. Evolutionary Computation, 17(3):(to appear), 2009. N. Krasnogor and J.E. Smith. Memetic algorithms: The polynomial local search complexity theory perspective. Journal of Mathematical Modelling and Algorithms, 7:3-24, 2008. All material available at www.cs.nott.ac.uk/~nxk/publications.html Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 3. Outline of the Talk –  So What Are Memetic Algorithm? –  A Pattern Language for Memetic Algorithms –  The Future of MAs –  Conclusions, Q&A Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 4. So What Are Memetic Algorithms? o  A MA is a carefully orchestrated interplay between (stochastic) global search and (stochastic) local search algorithms Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 5. So What Are Memetic Algorithms? Adding Domain Knowledge to EAs Memetic Algorithms (MAs) were originally inspired by: •  Models of adaptation in natural systems that combine evolutionary adaptation of population of individuals (GAs) WITH •  Individual learning (LS) within a lifetime (others consider the LS as development stage). Learning took the form of (problem specific) local search PLUS •  R. Dawkin’s concept of a meme which represents a unit of cultural evolution that can exhibit refinement, hence the local search can be adaptive. Is that right? Who does that? Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 6. •  MAs are one of the key methodologies behind successful discrete/continuous optimisation –  Nature Inspired Methodology? –  A Research Paradigm •  Key Design Issues underpinning MAs •  A Pattern Language for MAs design Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 7. Outline of the Talk –  So What Are Memetic Algorithm? –  A Pattern Language for Memetic Algorithms –  The Future of MAs –  Conclusions, Q&A Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 8. The Canonical MA At design From Eiben’s & Smith “Introduction To Evolutionary Computation” time lots of issues arise Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 9. Design Patterns and Pattern Languages In Alexander, C., Ishikawa, S., Silverstein, M., Jacobson, M., Fiksdahl-King, I., Angel, S.: A Pattern Language - Towns, Buildings, Construction. Oxford University Press (1977): “In this book, we present one possible pattern language,... The elements of this language are entities called patterns. Each pattern describes a problem which occurs over and over again in our environment, and then describes the core of the solution to that problem, in such a way that you can use this solution a million times over, without ever doing it the same way twice. “ Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 10. How are Patterns Described? A collection of well High Level defined patterns, i.e. a •  Pattern name rich pattern language, substantially enhances •  Problem statement our ability to •  The solution communicate solutions •  The Consequences to recurring problems without the need to •  Examples discuss specific implementation details. Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 11. Invariants and Decorations A Compact “Memetic” Algorithm by Merz (2003) Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 12. Invariants and Decorations A “Memetic” Particles Swarm Optimisation by Petalas et al (2007) Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 13. Invariants and Decorations A “Memetic” Artificial Immune System by Yanga et al (2008) Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 14. Invariants and Decorations A “Memetic” Learning Classifier System by Bacardit & Krasnogor (2009) Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 15. Invariants and Decorations •  Many more based on Ant Colony Optimisation, NN, Tabu Search, SA, DE, etc. •  Key Invariants: –  Global search mode –  Local search mode •  Many Decorations, e.g.: –  Crossover/Mutations (EAs based MAs) –  Pheromones updates (ACO based MAs) –  Clonal selection/Hypermutations (AIS based MAs) –  etc Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 16. Memetic Algorithm Pattern (MAP) •  Problem: how to successfully orchestrate local and global search •  Solution: for a given domain find exploration and exploitation mechanisms that work in synergy. •  Consequence: increase CPU? Diversity lost? •  Examples: too many! Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 17. Template Patterns The high-level MA pattern can be refined through multiple “Template Patterns” Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 18. Evolutionary Memetic Algorithm Template Pattern (EMATP) •  Problem: Achieving synergy between an EA (global search) and a problem specific heuristic •  Solution: standard cycle of I  Eval  Mate  Mut  Select is expanded with H, A or E methods at one or more of the stages. •  Consequence: if naively implemented results in diversity crisis and/or increased CPU time •  Examples: literature is rich in examples Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 19. Strategy Patterns •  The MAs template pattern can be refined through strategy patterns •  Strategy Patterns represent a family of interchangeable algorithms There are multiple strategy patterns in the MAs’ pattern language! Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 20. Refinement Strategy Pattern (RSP) •  Problem: what local search heuristic should be used? i.e., what’s the fitness landscape to employ? •  Solution: must consider the graph structure, the assignment of fitness labels and of navigation strategies. Must allow for obtaining/avoiding deep local optima, navigate large neutral plateaus, etc. •  Consequence: must understand multiple fitness landscapes, mean and worst case path to optima (PLS results), etc. •  Examples: SA-LS, Multimeme Algorithms, Variable Depth Search by Smith, Krasnogor, Sudhold, etc Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 21. Refinement Strategy Pattern (RSP) The choice of LS/Heuristic is one of the most important steps in the design of an MA 1.  Local searchers induce search landscapes and there has been various attempts to characterize these. Kallel et.al. and Merz et.al. have shown that the choice of LS can have dramatic impact on the efficiency and effectiveness of the MA 2.  Krasnogor formally proved that to reduce the worst case run time of MAs LS move operators must induce search graphs complementary (or disjoint) than those of the crossover and mutation. 3.  Krasnogor and Smith have also shown that the optimal choice of LS operator is not only problem and instance dependent but also dependent on the state of the overall search carried by the underlying EA The obvious way to implement 2&3 is to use multiple local searchers within an MA (multimeme algorithms) and we will see that the obvious way of including feedback like that suggested by 1 is to use self-generated multiple local searchers (self-generating MAs aka co-evolving MAs) Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 22. Refinement Strategy Pattern (RSP) Thanks to P. Merz! Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 23. Exact and Approximate Hybridisation Strategy Pattern (EAHSP) •  Problem: Hybridisation strategy different than for heuristic methods. Usually E&A methods are cpu hungry •  Solution: loose integration/tandem or tighter integration. •  Consequences: effort balance must be carefully calibrated. Sometimes the exact method is relaxed into beam search. Tradeoff between effort in building good enough models and guaranteed solutions must be analysed. •  Examples: Gallardo et al (2007), Mezmaz et al (2007), Raidl et al (2008), Pirkwieser (2008), etc Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 24. Population Diversity Handling Strategy Pattern (PDHSP) •  Problem:handling diversity is a critical issue as both RSP and EAHSP tend to focus search and hence promote diversity loss •  Solution: smart initialisations, tabu-like and archive-like mechanisms to avoid re-sampling, adaptive operators, multiple operators, age monitoring, diversity tracking at G,P & F levels, etc. •  Consequences: care must be put on what one wants high/low diversity to imply in terms of search behaviour. •  Examples: Neri et al (2007), Burke & Landa Silva (2004), Gustafson et al (2006), etc Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 25. Population Diversity Handling Strategy Pattern (PDHSP) Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 26. Population Diversity Handling Strategy Pattern (PDHSP) There are various ways to improve diversity (assuming that’s what one wants!): • if the population is seeded only do so partially. • instead of applying LS to every individual choose whom to apply it to. • use variation operators that ensure diversity (assorted) • in the local search strategy include a diversity weigth • modify the selection operator to prevent duplicates • archives • modify the acceptance criteria in the local search Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 27. Surrogate Objective Function Strategy Pattern (SOFSP) •  Problem: how to replace an expensive, noisy or unknown fitness functions? •  Solution:weighted histories, neural networks, fitness inheritane, reduction of variance techniques (e.g. latin hypercubes sampling), DOE, regresion models, etc. •  Consequences: must consider the level at which surrogacy will be implemented, e.g., objective function or problem itself? Are local or global approximation be used?, etc •  Examples: (also called metamodels, local models and partial objective functions) Battacharya (2007), Bull (1999), Paenke and Jin (2006), Zhou et al (2007), etc Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 28. Continuous Problems Refinement Strategy Pattern (CPRSP) •  Problem: Local optimum detection and, more generally, search scale is a critical issue •  Solution: methods include derivative-based and derivative-free, truncated searches and selective application of LS, LS intensity and frequency, •  Consequences: very difficult to a priori know the above parameters, hence, best course of action is (self)adaptation. Multimeme algorithms most successfull •  Examples: Ong & Keane (2004), Lozano et al. (2004), Hart (2005), etc Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 29. Multimeme Strategy Pattern (MSP) •  Problem: impossible to decide a priori best refinement, a method (and its parameters) to use. •  Solution: Use adaptation and self-adaptation on the methods themselves (rather than simply on the parameters). The MAP is provided with multiple LSs and a learning mechanism to adapt to problem, instance and stage of search. •  Consequences: Bookkeeping mechanism is needed. Reinforcement learning, neural network, LCS, etc. must be tightly integrated to EMATP. Simple schemes, however, very effective and cheap. •  Examples: Krasnogor & Smith (2001,2005,2008), Jakob (2006), Neri et al (2007), etc Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 30. Multiple Local Searchers Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 31. All the previous methodologies clearly benefits the end user as they have been shown to provide improvements in robustness, quality, etc. But what do we do if we do not have, or don’t know, good heuristics which could be used by,eg., A-teams, VNS or CLS? Also, why don’t we use the information the algorithm produces to better understand and make explicit new knowledge of the search landscape capturing this knowledge in new operators? Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 32. Self-Generating Strategy Pattern (SGSP) The use of knowledge is essential for the success of a search methods There are essentially two stages when knowledge is used: •  At design time: eg, in the form of local searchers/heuristics, specific variation operators, initialization biases, etc. •  At run time: •  using tabu-like mechanisms to avoid revisiting points (explicit) •  using adaptive operators that bias search towards unseen/promising regions of search space (implicit) •  creating new operators on-the-fly, eg., self-generating or co-evolving MAs (implicit) With appropriate data-mining techniques we can turn implicit knowledge into explicit and feed it back into the design process!  Deb’s “Innovisation” Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 33. Self-Generating Strategy Pattern (SGSP) •  Problem: How to implement a search mechanism that learns how to search in a reusable manner? •  Solution:To use (GB)ML to, given problem instances, capture problem-solving algorithmic building blocks. GP is a perfect candidate for this •  Consequences: only applicable in sufficiently hard problems and for instances that share common “patterns” •  Examples: Krasnogor & Gustafson (2002,2004), Smith (2002, 2003), Krasnogor (2004), Burke et al (2007), Fukunaga (2008) Tabacman et al (2008). Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 34. Methodologies On-the-fly operators discovery Canonical MA cycle Adapted from Durham, W.: Coevolution: Genes, Culture and Human Diversity. Stanford University Press (1991) Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 35. Methodologies On-the-fly operators discovery Self-Generating/Co-evolving Mas Adapted from Durham, W.: Coevolution: Genes, Culture and Human Diversity. Stanford University Press (1991) Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 36. What happens inside an MA? This is my solution to your problem I used strategy Population of agents X When I grow up I’ll need to decide whose problem Evolutionary Memetics Algorithm solving strategy to use Offspring Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 37. Methodologies On-the-fly operators discovery • Inheritance: an agent inherits the meme of the most successful of its parents There are various processes that • Imitation: an agent imitates a successful non-genetically-related guide the Agent’s cultural individual evolution of local search strategies: • Innovation: an agent blindly (i.e.randomly) change its meme • Mental Simulation: an agent purposely (e.g. hill-climbs to ) improve its meme Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 38. From Krasnogor & Gustafson paper Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 39. Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 40. Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 41. Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 42. Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 43. From Smith paper Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 44. A Pattern Language for Memetic Algorithms Memetic Algorithms by N. Krasnogor. Handbook of Natural Computation (chapter) in Natural Computing. Springer Berlin / Heidelberg, 2009. www.cs.nott.ac.uk/~nxk/publications.html Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 45. Outline of the Talk –  So What Are Memetic Algorithm? –  A Pattern Language for Memetic Algorithms –  The Future of MAs –  Conclusions, Q&A Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 46. •  Software should be “seeded” and grown, very much like a plant or animal (including humans) •  Software should be embryonic and develop when situated on a production environment •  What would a software “incubation” machine look like? •  What would a software “germinator” look like? Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 47. Organs Individual Cells Tissue DNA/RNA Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 48. Production Environment Input TSP Organ SC TSP Software Cell SC Euclidean TSP Organ SC SC SC SC Solver Software Organism Pluripotential Solver “DNA” GraphicalTSP Organ Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 49. Outline of the Talk –  So What Are Memetic Algorithm? –  A Pattern Language for Memetic Algorithms –  The Future of MAs –  Conclusions, Q&A Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 50. Conclusions (I) • There is much more in MA that meets the eye. Its not a simple matter of ad-hoc putting LS somewhere in the EA cycle. • Just a small space of the architectural space of MAs has been explored and we don’t know yet why a given architecture performs well/bad in a specific problem • People usually use one “silver bullet” LS. That’s fine if that SB exists. However when it does not exist use multimeme algorithms, or other heuristics teams/cooperative algorithms as lots of simple heuristics can synergistically do the trick. Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 51. Conclusions (II) • ADAPT: the search process is dynamic and your method should detect and adapt to changing circumstances. Adaptation is not too expensive or complex to code! • Carefully consider how your variation operators interact with LS •  Consider whether Baldwinian or Lamarckianism is better • Understand that the fitness landscape explored by your MA is not a one-operator landscape but the results of the superposicion with interference of varios landscapes. Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 52. Conclusions (III) • Use more expresive acceptance criteria in your local search, eg., fuzzy criteria • If you don’t know what operators to apply let the the MA find it for you by some Self-Generating mechanism, e.g., co-evolution. • Self-Generating mechanisms are a great niche for GPers! FINALLY: check out the literature, almost surely you will find MAs among the best success stories in applications to real world problems! Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada
  • 53. Automated Heuristic Design: Crossing The Chasm – GECCO 2009 Workshop – Invited Speaker Presentation – 9/July/2009 – Montreal, Canada