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EA*A Hybrid Approach
EA*A Hybrid Approach
Robbie HansonRobbie Hanson
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What is it?What is it?
The A* algorithm, using an EA for the heuristic.
An efficient way of partitioning the search space for an EA.
An effective termination technique for EA’s.
The A* algorithm, using an EA for the heuristic.
An efficient way of partitioning the search space for an EA.
An effective termination technique for EA’s.
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MotivationMotivation
Poor EA performanceLocal maxima/minima traps!Balancing exploration with exploitation.
When to terminate?
Poor EA performanceLocal maxima/minima traps!Balancing exploration with exploitation.
When to terminate?
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The General IdeaThe General Idea
Partition the search spaceExplore each partitionContinue exploration on “promising” partitions
Partition the search spaceExplore each partitionContinue exploration on “promising” partitions
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Motivation (cont)Motivation (cont)
Hybridisation… (Chapter 10)“This category of algorithms is very successful in practice and forms a rapidly growing research area with great potential.”
Hybridisation… (Chapter 10)“This category of algorithms is very successful in practice and forms a rapidly growing research area with great potential.”
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Intro to A*Intro to A*
Branch and bound techniqueExtension of best-first search of a tree
Uses heuristic to determine fitness of nodes
Branch and bound techniqueExtension of best-first search of a tree
Uses heuristic to determine fitness of nodes
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Example TreeExample Tree
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Problems with A*Problems with A*
A* relies on a good heuristicWithout it, it becomes essentially a breadth first search
Some problems are difficult to design standard heuristics for
“For every common sense heuristic you can invent, you can find a pathological case that will make it look very silly.”
(Michalewicz & Fogel: “How to solve it: Modern Heuristics”)
A* relies on a good heuristicWithout it, it becomes essentially a breadth first search
Some problems are difficult to design standard heuristics for
“For every common sense heuristic you can invent, you can find a pathological case that will make it look very silly.”
(Michalewicz & Fogel: “How to solve it: Modern Heuristics”)
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Traveling Salesman Problem
Traveling Salesman Problem
The traveling salesman must visit every city in his territory exactly once and then return home covering the shortest distance.
Search space: (N-1)! / 210-city: 181,000 solutions20-city: 10,000,000,000,000,000 solutions
TSPlib contains many real world examples
The traveling salesman must visit every city in his territory exactly once and then return home covering the shortest distance.
Search space: (N-1)! / 210-city: 181,000 solutions20-city: 10,000,000,000,000,000 solutions
TSPlib contains many real world examples
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ExampleExample
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ExampleExample
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ExampleExample
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ExampleExample
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EA DetailsEA Details
Parameter file specifies specifics, such as population size, number of children, etc.
Log file captures output.This facilitates experimentation of parameter values.
Parameter file specifies specifics, such as population size, number of children, etc.
Log file captures output.This facilitates experimentation of parameter values.
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Representation and Fitness
Representation and Fitness
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Selection and SurvivalSelection and Survival
Tournament SelectionSelection size specified in parameter file
(µ + λ) survival strategy
Tournament SelectionSelection size specified in parameter file
(µ + λ) survival strategy
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Recombination/MutationRecombination/Mutation
Single parent mutation most popular
Two popular methods
Single parent mutation most popular
Two popular methods
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EA* specificEA* specific
Number of generations to run EA for each iteration.
How long may a node remain in the “open list?”
Number of generations to run EA for each iteration.
How long may a node remain in the “open list?”
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PerformancePerformance
Final solutions are VERY consistent.
Initial results suggest a lower standard deviation than regular EA.
SO FAR, it averages better solutions. (Very difficult to say)
Final solutions are VERY consistent.
Initial results suggest a lower standard deviation than regular EA.
SO FAR, it averages better solutions. (Very difficult to say)
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ProblemsProblems
Large TSP problemsLucky first guesses
Large TSP problemsLucky first guesses
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Future ResearchFuture Research
EA’s report expected fitness in generations to come.
This could help the EA to overestimate less often, possibly making the heuristic admissible for A*.
Local search techniques in the EA for better performance.
Trivial parallelization. (BOINC?)
EA’s report expected fitness in generations to come.
This could help the EA to overestimate less often, possibly making the heuristic admissible for A*.
Local search techniques in the EA for better performance.
Trivial parallelization. (BOINC?)
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Questions?Questions?