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Constituent Grammatical Evolution (CGE)
An improvement of GE implemented with jGE & NetLogo
LOUKAS GEORGIOU and WILLIAM J. TEAHAN
Artificial Intelligence and Intelligent Agents Research Group
School of Computer Science, Bangor University, U.K.
[email protected], [email protected]
Overview
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Introduction
Grammatical Evolution (GE)
Constituent Grammatical Evolution (CGE)
CGE Implementation
Benchmarking Problems
Experimental Results
Conclusions & Future Work
Introduction
Goals
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Tackling the destructive crossover and genotype bloating issues of GE
Improving GE performance in terms of both effectiveness and efficiency
CGE at a glance
Constituent Grammatical Evolution takes inspiration from two very elementary
concepts: genes and conditional behaviour switching. It augments the
standard Grammatical Evolution algorithm by incorporating and utilising
these concepts in order to improve the performance of the later in agent
problems.
CGE Unique Features
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Constituent Genes
Conditional Behaviour-Switching
Restriction of the genotype size
Grammatical Evolution
The Grammatical Evolution system
Grammatical Evolution is an evolutionary algorithm that can
evolve complete programs in an arbitrary language using
a variable-length binary string. The binary string
(genome) determines which production rules in a Backus
Naur Form (BNF) grammar definition are used in a
genotype-to-phenotype mapping process to a program.
Grammatical Evolution
The GE Mapping Process
1. The genotype is used to map
the start symbol of the BNF
Grammar into terminals
2. It reads “codons” of 8 bits and
generates the corresponding
integer (RNA) each time a
non-terminal to be translated
has more than one production
rules
3. The selected production rule
is calculated with the formula:
rule = [codon integer value]
MOD [number of rules for the
current non-terminal]
Grammatical Evolution
GE Issues
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Destructive Crossovers
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Low locality of genotype-to-phenotype mapping
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Genotype bloating
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Very poor performance on the SFT when the original
search space is used (as it were defined by Koza)
Constituent Grammatical Evolution
CGE constitutes a conditional behaviour switching evolutionary
algorithm, based on the Grammatical Evolution algorithm, and which
incorporates the notion of genes in the individual’s genotype.
Main Characteristics
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The conditional behaviour switching approach, which biases
the search space toward useful areas.
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Incorporation of the notion of genes, which tackles the issue of
destructive crossovers and provides useful reusable building blocks.
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Restriction of the genotype size, which tackles the genotype
bloat phenomenon.
Constituent Grammatical Evolution
CGE Inputs
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Problem Specification
Language Specification
Behaviour-Switching Specification
Grammatical Evolution algorithm
CGE Parameters
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GE Standard Parameters
Genes Pool Size, Genes Generations,
Gene Code Iterations, Gene Evaluations
Genes Codons Min, Genes Codons Max, Genes Max Wraps
Constituent Grammatical Evolution
Sample BNF Grammar Definitions
GE BNF-Koza Grammar Definition for the SFT problem
<expr> ::= <line> | <expr> <line>
<line> ::= ifelse food-ahead [ <expr> ][ <expr> ] | <op>
<op> ::= turn-left | turn-right | move
GE BNF-O’Neill Grammar Definition for the SFT problem
<code> ::= <line> | <code> <line>
<line> ::= <condition> | <op>
<condition> ::= ifelse food-ahead [ <line> ][ <line> ]
<op> ::= turn-left | turn-right | move
CGE BNF-BS Grammar Definition for the SFT problem
<behaviour> ::= ifelse food-ahead [ <op> ] [ <op> <behaviour> ] |
ifelse food-ahead [ <op> ][ <op> ] | <op>
<op> ::= turn-left | turn-right | move | ... {Constituent Genes Phenotypes}(*)
CGE Implementation
• Java & NetLogo
• jGE - Java Grammatical Evolution
• jGE NetLogo Extension
• CGE NetLogo models
jGE - Official Web Site
jGE NetLogo Extension
• Extension of jGE for NetLogo
• NetLogo users can use now GE
• GE researchers have now a modeling
environment for experiments
• Available from jGE and NetLogo web sites
CGE NetLogo Models
Benchmarking Problems
• Santa Fe Trail
• Los Altos Hills
• Hampton Court Maze
The Santa Fe Trail Problem
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A standard benchmark problem
Full of local optima and with many plateaus
Deceptive at all levels
Find all 89 pieces of food
Limited number of steps
Square 32x32 toroidal grid
Three Operations
– move, turn left, turn right
• One Sensor
– food ahead
The Los Altos Hills Problem
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A more challenging version of the SFT
Introduces two new kinds of irregularity
157 pieces of food
100x100 toroidal grid
Trail
– 221 squares
– 29 turns
• Three Operations
– move, turn left, turn right
• One Sensor
– food ahead
The Hampton Court Maze Problem
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Simple connected maze searching problem
Find a route to the exit of the maze
Grid size 39x23
Three Operations
– move
– turn left
– turn right
• Three Sensors
– wall ahead
– wall left
– wall right
Experimental Results (1)
Santa Fe Trail Problem
500
CGE
GE using BNF-O'Neill
GE using BNF-Koza
450
400
Cumulative Frequency
350
300
250
200
150
100
50
0
0
2
4
6
8
10
12
14
16
18
20
22
24
Generation
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28
30
32
34
36
38
40
42
44
46
48
50
Experimental Results (2)
Santa Fe Trail Problem
Exp #1
Exp #2
Exp #3
Exp #4
Exp #5
Best
Runs
100
100
100
100
100
100
Steps
393
375
393
377
337
337
Success
85%
93%
89%
94%
87%
94%
90%
Avg. Success
Exp #1
Exp #2
Exp #3
Exp #4
Exp #5
Best
Runs
100
100
100
100
100
100
Steps
419
507
415
541
479
415
Success
8%
11%
10%
6%
13%
13%
GE using BNF-Koza
10%
Avg. Success
Exp #1
Exp #2
Exp #3
Exp #4
Exp #5
Best
Runs
100
100
100
100
100
100
Steps
609
609
607
609
607
607
Success
80%
76%
75%
81%
74%
81%
Avg. Success
CGE
78%
GE using BNF-O’Neill
Experimental Results (3)
Los Altos Hills Problem
Runs
Best Solution’s Steps
Success Rate
100
CGE
GE using BNF-Koza
GE using BNF-O’Neill
100
100
100
1093
No solution
No solution
9%
0%
0%
GE using BNF-O'Neill
GE using BNF-Koza
CGE
90
Cumulative Frequency
80
70
60
50
40
30
20
10
0
0
2
4
6
8
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Generation
Experimental Results (4)
Hampton Court Maze Problem
CGE
GE using BNF-Koza
GE using BNF-O’Neill
Runs
100
100
100
Best Solution’s Steps
384
439
494
Success Rate
82%
1%
1%
100
CGE
GE using BNF-O'Neill
GE using BNF-Koza
90
80
Cumulative Frequency
70
60
50
40
30
20
10
0
0
1
2
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10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Generation
Conclusions & Future Work
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CGE builds from elementary and more complex building blocks a control program
which dictates the behaviour of an agent.
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Due to the constituent genes and genotype size limit features, the Grammatical
Evolution issues of destructive crossover events and genotype bloating are tackled
respectively.
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Experimental results show that CGE outperforms GE in terms of both efficiency
(percent of solutions found) and effectiveness (number of required steps of solutions
found) in all three benchmarking problems (SFT, LAH, HCM).
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Further work includes the benchmarking of CGE in “real-world” problems and the
evaluation of alternative, and probably more efficient, mechanisms for the creation of
constituent genes.
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Indeed, the application of the CGE main concepts in other Evolutionary Algorithms
will be investigated.
Thank You!
Any questions?
(Also come along to see our poster from
10:30 to 12:10 tomorrow)
Constituent Grammatical Evolution
Artificial Intelligence
and Intelligent Agents
Research Group