AUTOMATED REAL-WORLD PROBLEM SOLVING EMPLOYING META-HEURISTICAL BLACK-BOX OPTIMIZATION November 2013 Daniel R. Tauritz, Ph.D. Guest Scientist, Los Alamos National Laboratory Director, Natural Computation Laboratory Department of Computer Science Missouri University of Science and Technology
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AUTOMATED REAL-WORLD PROBLEM SOLVING EMPLOYING META-HEURISTICAL BLACK-BOX OPTIMIZATION November 2013 Daniel R. Tauritz, Ph.D. Guest Scientist, Los Alamos.
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AUTOMATED REAL-WORLD PROBLEM SOLVING EMPLOYING META-
HEURISTICAL BLACK-BOX OPTIMIZATION
November 2013
Daniel R. Tauritz, Ph.D.Guest Scientist, Los Alamos National Laboratory
Director, Natural Computation LaboratoryDepartment of Computer Science
Missouri University of Science and Technology
Black-Box Search Algorithms
• Many complex real-world problems can be formulated as generate-and-test problems
• Black-Box Search Algorithms (BBSAs) iteratively generate trial solutions employing solely the information gained from previous trial solutions, but no explicit problem knowledge
Practitioner’s Dilemma
1. How to decide for given real-world problem whether beneficial to formulate as black-box search problem?
2. How to formulate real-world problem as black-box search problem?
3. How to select/create BBSA?
4. How to configure BBSA?
5. How to interpret result?
6. All of the above are interdependent!
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Theory-Practice Gap
• While BBSAs, including EAs, steadily are improving in scope and performance, their impact on routine real-world problem solving remains underwhelming
• A scalable solution enabling domain-expert practitioners to routinely solve real-world problems with BBSAs is needed
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Two typical real-world problem categories
• Solving a single-instance problem:
automated BBSA selection
• Repeatedly solving instances of a problem class:
evolve custom BBSA
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Part I: Solving Single-Instance Problems
Employing Automated BBSA Selection
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Requirements
1. Need diverse set of high-performance BBSAs
2. Need automated approach to select most appropriate BBSA from set for a given problem
3. Need automated approach to configure selected BBSA
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Automated BBSA Selection
1. Given a set of BBSAs, a priori evolve a set of benchmark functions which cluster the BBSAs by performance
2. Given a real-world problem, create a surrogate fitness function
3. Find the benchmark function most similar to the surrogate
4. Execute the corresponding BBSA on the real-world problem
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Benchmark Generator
BBSA1 BBSA2 BBSAn…
BBSA1 BP1 BBSA2 BP2 BBSAn BPn…
A Priori, Once Per BBSA Set
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A Priori, Once Per Problem ClassReal-World Problem
Sampling Mechanism
Surrogate Objective Function
Match with most “similar” BPk
Identified most appropriate BBSAk
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Per Problem InstanceReal-World Problem
Sampling Mechanism
Surrogate Objective Function
Apply a priori established most appropriate BBSAk
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Requirements
1. Need diverse set of high-performance BBSAs
2. Need automated approach to select most appropriate BBSA from set for a given problem
3. Need automated approach to configure selected BBSA
Static vs. dynamic parameters
• Static parameters remain constant during evolution, dynamic parameters can change
• Dynamic parameters require parameter control
• The optimal values of the strategy parameters can change during evolution [1]
• Created novel meta-GP approach for evolving BBSAs tuned to specific problem classes
• Ideal for solving repeated problems• Evolved custom BBSA which
outperformed standard EA and hill-climber on all tested problem instances
• Future work includes adding additional primitives and testing against state-of-the-art BBSAs on more challenging problems
Summary
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Take Home Message
• Practitioners need automated algorithm selection & configuration
• The number of BBSAs is increasing rapidly, making the selection of the best one to employ for a given problem increasingly difficult
• Some recent BBSAs facilitate automated real-world problem solving
References[1] Brian W. Goldman and Daniel R. Tauritz. Meta-Evolved Empirical Evidence of
the Effectiveness of Dynamic Parameters. In Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '11), pages 155-156, Dublin, Ireland, July 12-16, 2011.
[2] Brian W. Goldman and Daniel R. Tauritz. Self-Configuring Crossover. In Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '11), pages 575-582, Dublin, Ireland, July 12-16, 2011.
[3] Brian W. Goldman and Daniel R. Tauritz. Supportive Coevolution. In Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '12), pages 59-66, Philadelphia, U.S.A., July 7-11, 2012.
[4] Nathaniel R. Kamrath, Brian W. Goldman and Daniel R. Tauritz. Using Supportive Coevolution to Evolve Self-Configuring Crossover. In Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '13), pages 1489-1496, Amsterdam, The Netherlands, July 6-10, 2013.
[5] Matthew A. Martin and Daniel R. Tauritz. Evolving Black-Box Search Algorithms Employing Genetic Programming. In Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '13), pages 1497-1504, Amsterdam, The Netherlands, July 6-10, 2013.