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Bi-directional incremental evolution

Bi-directional incremental evolution. Dr Tatiana Kalganova Electronic and Computer Engineering Dept. Bio-Inspired Intelligent Systems Group Brunel University. Outline. Evolutionary process Evolvable hardware Bi-directional incremental evolution: basic concept

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Bi-directional incremental evolution

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  1. Bi-directional incremental evolution Dr Tatiana Kalganova Electronic and Computer Engineering Dept. Bio-Inspired Intelligent Systems Group Brunel University

  2. Outline • Evolutionary process • Evolvable hardware • Bi-directional incremental evolution: basic concept • Bi-directional incremental evolution in evolvable hardware • Some applications of bi-directional incremental evolution

  3. What is an evolution? Evolution Selection Crossover Mutation Chromosome: DNA Chromosome: DNA

  4. What is an evolvable hardware (EHW)? • The logic circuit is designed using evolutionary algorithm Evolution Selection Crossover chromosome Mutation chromosome

  5. Circuit design problem in evolvable hardware

  6. “Stalling” effect in evolutionary process Mult2.pla - 5.000 generations Mult3.pla – 16.000.000 generations Mult4.pla - ? … 150.000.000.000 ?

  7. “Stalling” effect in evolutionary process • REASON: the task is too complex to solve at once. • SOLUTION: introduce the new evolutionary process once the “stalling” effect is appeared • IMPLEMENTATION: the new fitness function is used for each evolutionary process

  8. Bi-directional Incremental Evolution • IDEA: two directions of evolution to obtain the desired solution • CONCEPT: evolve the system from complex to simple and optimise using evolution from simple to complex • REQUIREMENTS: knowledge of system evolved and identification of heuristics • SUCCESS: use of simple different evolutionary processes identified by various heuristics • EXAMPLE: evolvable hardware

  9. Stage 1: Evolution towards a modularised system IDEA: Evolution performs from complex system to sub-systems Stage 2: Evolution towards an optimised system IDEA: Evolution performs from sub-systems to complex system Bi-directional Incremental Evolution

  10. Bi-directional Incremental Evolution (BIE) in EHW

  11. Idea Evolve the system gradually using decomposition methods 1) Decompose the system into sub-systems 2) Evolve each sub-system separately 3) Assemble the complex system 4) Evolve the complex system Advantages Evolving the circuits of the large number of variables Evolving the circuits of any complexity No restrictions on the application task Bi-directional Incremental Evolution (BIE) for EHW

  12. BIEin EHW

  13. BIE:EHW-oriented decomposition

  14. BIE: sub-circuit allocation

  15. Direct and incremental evolutions

  16. BIE in applications • Evolution of complex combinational logic circuits • Optimisation of control the fermentation process • Prediction in investment appraisal

  17. BIE in prediction in investment appraisal • Problem: • Design an Intelligent System for Risk Classification of Stock Investment Projects

  18. Training network • An effective bi-directional evolutionary strategy is elaborated, as direct evolution fails to rich a solution to the complex problem of optimising the weights and shift terms in the fuzzy network over a set of investment projects.

  19. BIE • The strategy involves a decomposition and an incremental part. • The integral problem is first divided into subtasks of decreasing complexity by partitioning accordingly the training set of projects. • Then the subtasks are merged incrementally to optimise the integral solution.

  20. Training-set partitioning and increment during bidirectional incremental evolution Decomposition part: the training set is partitioned at several levels, evolving the fuzzy network towards tasks with decreasing complexity. Incremental part: the training subsets are merged incrementally in reverse direction, evolving the network towards solving the integral problem. A dynamic objective function is applied at each decomposition and incremental level. Training partitioning

  21. incrementalpart decomposition part bidirectional incremental evolution direct evolution Performance of bidirectional incremental evolution and direct evolution in maximum fitness per generation Black line: bidirectional incremental evolution advances through several decomposition and incremental tasks and solves the general problem in 148,243 generations. Lighter line: direct evolution makes some initial progress and then stalls. BIE results

  22. Experimental results • The bi-directional strategy evolves a fully functional fuzzy network in 148,243 generations. • Direct evolution reaches only 46.33% maximum fitness in 500,000 generations. • Thus, the empirical results prove decisively the efficiency of the developed evolutionary strategy.

  23. Some results • In all applications mentioned earlier it has been obtained that the optimal solution has been obtained at least in 100 times quicker then using standard evolution • The quality of evolved solution in this case remains the same

  24. BIE in applications: Summary • Design of complex combinational circuits • Use of • Decomposition methods • Evolutionary strategy

  25. BIE in applications: Summary • Prediction in investment appraisal • Use of • Decomposition methods • Automatic re-scaling fitness function • Neural network • Fuzzy logic

  26. Conclusion • Bi-directional incremental evolution is the technique that can be used in evolution of complex systems • BIE allows to use evolutionary algorithms in both online and offline calculations • BIE can be used in large range of applications

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