Advanced Techniques for Solving Optimization Problems through Evolutionary Algorithms

Transcription

1 PhD Final Presentation Advanced Techniques for Solving Optimization Problems through Evolutionary Algorithms Advisor: Giovanni Squillero PhD Candidate: Marco Gaudesi CAD Group DAUIN February 27 th, 2015

2 Outline Intro: Evolutionary Algorithms (EAs) µgp EA Theory DMAB Group Evolution Distance Metric EA Practice Q & A Test Programs Generation Test Programs Compaction Virus WetLand

3 Distance Metric DMAB Group Evolution Evolutionary Computation Test Generation EA Test Compaction Test Compaction Test of Reorder Buffer Memory Iterated Prisoner s DIlemma WetLand Virus

4 EAs Evolutionary Algorithm Useful when we know the final goal, but not how to reach it Evolution is not a random process - Random mutations afford materials - Objective evaluation: only useful mutations are preserved - The process requires to assess the final effect of modifications, not to design them - EAs are stochastic optimization techniques EAs are population based

5 MicroGP µgp 3 Framework A computational approach for pursuing a goal defined by the user First task: creation of assembly-language programs for testing microprocessors The user is required to evaluate the candidate solutions proposed by the toolkit µgp 3 characteristics were exploited and modified to implement new technologies discussed within this thesis

6 THEORY

7 EA Theory EC theory: PDMAB, operators selection The Dynamic Multi-Armed Bandit mechanism (DMAB) is used to select best operators to be applied during evolution State-of-the-art: standard MAB algorithm require a big amount of time to detect that the best operator has changed? It was proposed to use a statistical change detection test, creating the Dynamic MAB (DMAB) algorithm Activity and improvements:? Modification of the DMAB selection strategy: Allowing operator to sporadically fail No performance feedback after each application Publication [submitted]: J. Belluz, M. Gaudesi, G. Squillero, A. Tonda Operator Selection using Improved Multi-Armed Bandit, GECCO 2015.?

8 EA Theory EC theory: Group Evolution New EA technique, introducing the concept of group State-of-the-art: EAs use a single-individual solution-oriented approach Activities: New group operators New fitness function: it takes into account the overlapping of individuals within a group Example based on strings coverage presented by Banzhaf

9 PHENOTYPE GENOTYPE EA Theory EC theory: Genotype / Phenotype distance Universal Information Distance (UID): the distance between two individuals is the number of distinct symbols they do not have in common. ADD AX, BX State-of-the-art: Distance between individuals is calculated at phenotype level, or comparing also fitness Activities: Definition of symbol at genotype level Investigation on new approaches to calculate the difference among lists of symbols Results: Strong correlation with phenotype-level in two problems where individuals represent string of bits and assembly language programs Publications: M. Gaudesi, G. Squillero, A. Tonda An Efficient Distance Metric for Linear Genetic Programming, GECCO 2013 M. Gaudesi, G. Squillero, A. Tonda Universal information distance for genetic programming, GECCO SUB 55, CX JNZ individuala.s label1: ADD AX, BX SUB 55, CX JNZ label1

10 PRACTICE

11 EA Practice Automated Wetland Design Intro: Wetland: artificial ponds, able to filter water through plants State-of-the-art: Traditionally designed by expert, through a trial & error approach Activity: Proposing an automatic approach to wetland design, exploiting an evolutionary algorithm. Developed in collaboration with the University of Padova and INRA Research Center (Thiverval-Grignon, France) Publications: M. Gaudesi, A. Marion, T. Musner, G. Squillero, A. Tonda Evolutionary Optimization of Wetlands Design, 28th Annual ACM Symposium on Applied Computing, SAC 2013, Coimbra, Portugal M. Gaudesi, A. Marion, T. Musner, G. Squillero, A. Tonda An Evolutionary Approach to Wetland Design, Evo* 13

12 EA Practice Evolving malware Generation of individuals (viruses) in an hostile environment, monitored by antivirus Activities: Code Generation: starting from a database formed by code of several malicious applications, the final goal of the evolution is to create a malware not detected by anti-virus Code Integration: the EA is used to determine the optimal position for hiding malicious code inside an existing executable Evolvable routine to encrypt malware Publication: A. Cani, M. Gaudesi, E. Sanchez, G. Squillero, A. Tonda Towards Automated Malware Creation: Code Generation and Code Integration, ACM 29 th Symposium On Applied Computing (SAC 2014), 2014.

13 EA Practice Group Evolution [to minimips] Intro: Theoretical approach to a new EA technique, introducing the concept of group State-of-the-art: Currently, EAs use an approach single-individual solution-oriented Activity: Applied in CAD environment, in order to improve faults coverage of assembler programs for verifying minimips processor Publication: Ciganda L., Gaudesi M., Lutton E., Sanchez E., Squillero G., Tonda A. Automatic Generation of On-Line Test Programs through a Cooperation Scheme, 13th International Workshop on Microprocessor Testing and Verification (MTV 2012), Austin, Texas

14 EA Practice Test Programs Compaction The shorter is the execution time needed to run a test program and check the correctness of results, the better is the quality of the tests suite. Goal: Removing useless instructions within the test program, without affecting faults covered by the original one Activities: Definition of a simple approach to apply EA to compaction problem Definition of the fitness function, formed by two values: the first one is the number of non covered faults, to be minimized; the second one is the number of zeros (representing the useless instructions), to be maximized Evaluation of the effectiveness of this approach on the minimips processor core Publication: R. Cantoro, M. Gaudesi, E. Sanchez, P. Schiavone, G. Squillero, An Evolutionary Approach for Test Program Compaction, LATS 2015

15 PUBLICATIONS LIST Ciganda L., Gaudesi M., Lutton E., Sanchez E., Squillero G., Tonda A. Automatic Generation of On-Line Test Programs through a Cooperation Scheme, MTV 2012 M. Gaudesi, A. Marion, T. Musner, G. Squillero, A. Tonda Evolutionary Optimization of Wetlands Design, 28th Annual ACM Symposium on Applied Computing, SAC 2013 M. Gaudesi, A. Marion, T. Musner, G. Squillero, A. Tonda An Evolutionary Approach to Wetland Design, Evo* 13 M. Gaudesi, G. Squillero, A. Tonda An Efficient Distance Metric for Linear Genetic Programming, GECCO 2013 A. Cani, M. Gaudesi, E. Sanchez, G. Squillero, A. Tonda Towards Automated Malware Creation: Code Generation and Code Integration, SAC M. Gaudesi, M. Jenihhin, J. Raik, E. Sanchez, G. Squillero, V. Tihhomirov, R. Ubar Diagnostic Test Generation for Statistical Bug Localization using Evolutionary Computation, Evo* 14. S. Di Carlo, M. Gaudesi, E. Sanchez, M. Sonza Reorda A Functional Approach for Testing the Reorder Buffer Memory, JETTA Journal of Electronic Testing. M. Gaudesi, S. Saleem, E. Sanchez, M. Sonza Reorda, E. Tanowe, On the In-Field Test of Brench Prediction Units using the Correlated Predictor mechanism, DDECS 2014 M. Gaudesi, E. Piccolo, G. Squillero, A. Tonda, TURAN: Evolving non-deterministic players for the iterated prisoner s dilemma, CEC 2014 M. Gaudesi, G. Squillero, A. Tonda, Universal information distance for genetic programming, GECCO 2014 R. Cantoro, M. Gaudesi, E. Sanchez, P. Schiavone, G. Squillero, An Evolutionary Approach for Test Program Compaction, LATS 2015 M. Gaudesi, M. Sonza Reorda, I. Pomeranz On Test Program Compaction. In: ETS 15. [in press] N. Palermo, V. Tihhomirov, T.S. Copetti, M. Jenihhin, J. Raik, S. Kostin, M. Gaudesi, G. Squillero, M. Sonza Reorda, F. Vargas Rejuvenation of Nanoscale Logic at NBTI-Critical Paths Using Evolutionary TPG. LATS [In press] M. Gaudesi, E. Piccolo, G. Squillero, A. Tonda, Exploiting Evolutionary Modeling to Prevail in Iterated Prisoner s Dilemma Tournaments, IEEE Trans. on Computational Intelligence and AI in Games [minor review - submitted] Thanks for Your Attention