1 Genetic Algorithms “Genetic Algorithms are good at taking large, potentially huge search spaces and navigating them, looking for optimal combinations of things, solutions you might not otherwise find in a lifetime.” - Salvatore Mangano Computer Design, May 1995 Genetic Algorithms: Genetic Algorithms: Soft Computing Week 13 Soft Computing Week 13
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
1 Genetic Algorithms
“Genetic Algorithms are good at taking large,
potentially huge search spaces and navigating
them, looking for optimal combinations of things, solutions you might not
With some high probability (crossover rate) apply crossover to the parents.
17 Genetic Algorithms
Mutation
1011011111
1010000000
Offspring1
Offspring2
1011001111
1000000000
Offspring1
Offspring2
With some small probability (the mutation rate) flip each bit in the offspring (typical values between 0.1
and 0.001)
mutate
Original offspring Mutated offspring
18 Genetic Algorithms
Back to the (GA) Algorithm
Generate a population of random chromosomes
Repeat (each generation) Calculate fitness of each chromosome Repeat
» Use roulette selection to select pairs of parents» Generate offspring with crossover and mutation
Until a new population has been produced Until best solution is good enough
19 Genetic Algorithms
Many Variants of GA Different kinds of selection (not roulette)
Tournament Elitism, etc.
Different recombination Multi-point crossover 3 way crossover etc.
Different kinds of encoding other than bitstring Integer values Ordered set of symbols
Different kinds of mutation
20 Genetic Algorithms
Many parameters to set Any GA implementation needs to decide on a
number of parameters: Population size (N), mutation rate (m), crossover rate (c)
Often these have to be “tuned” based on results obtained - no general theory to deduce good values
Typical values might be: N = 50, m = 0.05, c = 0.9
21 Genetic Algorithms
Why does crossover work?
A lot of theory about this and some controversy
Holland introduced “Schema” theory The idea is that crossover preserves “good
bits” from different parents, combining them to produce better solutions
A good encoding scheme would therefore try to preserve “good bits” during crossover and mutation
22 Genetic Algorithms
Classes of Search Techniques
F in on acci N ew ton
D irect m eth ods Indirec t m ethods
C alcu lu s-based tech n iques
Evolu tion ary s trategies
C entra l ized D istribute d
Pa ra l le l
S tea dy-s ta te G enera tiona l
Seque ntia l
G e ne tic a lgori thm s
Evolutiona ry a lgori thm s Sim u lated ann ealin g
G uide d random se arc h te chnique s
D yn am ic program m in g
Enu m erative tech n iqu es
Se arch te chniques
23 Genetic Algorithms
Components of a GAA problem to solve, and ... Encoding technique (gene, chromosome) Initialization procedure (creation) Evaluation function (environment) Selection of parents (reproduction) Genetic operators (mutation, recombination) Parameter settings (practice and art)
select parents for reproduction;perform recombination and mutation;evaluate population;
}}
25 Genetic Algorithms
The GA Cycle of Reproduction
reproduction
population evaluation
modification
discard
deleted members
parents
children
modifiedchildren
evaluated children
26 Genetic Algorithms
Population
Chromosomes could be: Bit strings (0101 ... 1100) Real numbers (43.2 -33.1 ... 0.0 89.2) Permutations of element (E11 E3 E7 ... E1 E15) Lists of rules (R1 R2 R3 ... R22 R23) Program elements (genetic programming) ... any data structure ...
population
27 Genetic Algorithms
Reproductionreproduction
population
parents
children
Parents are selected at random with selection chances biased in relation to chromosome evaluations.
28 Genetic Algorithms
Chromosome Modification
modificationchildren
Modifications are stochastically triggered Operator types are:
Mutation Crossover (recombination)
modified children
29 Genetic Algorithms
Evaluation
The evaluator decodes a chromosome and assigns it a fitness measure
The evaluator is the only link between a classical GA and the problem it is solving
evaluation
evaluatedchildren
modifiedchildren
30 Genetic Algorithms
Deletion
Generational GA:entire populations replaced with each iteration
Steady-state GA:a few members replaced each generation
population
discard
discarded members
31 Genetic Algorithms
An Abstract Example
Distribution of Individuals in Generation 0
Distribution of Individuals in Generation N
32 Genetic Algorithms
A Simple Example
“The Gene is by far the most sophisticated program around.”
- Bill Gates, Business Week, June 27, 1994
33 Genetic Algorithms
A Simple Example
The Traveling Salesman Problem:
Find a tour of a given set of cities so that each city is visited only once the total distance traveled is minimized
34 Genetic Algorithms
RepresentationRepresentation is an ordered list of citynumbers known as an order-based GA.
1) London 3) Dunedin 5) Beijing 7) Tokyo2) Venice 4) Singapore 6) Phoenix 8) Victoria
Termination Criteria Performance, scalability Solution is only as good as the evaluation
function (often hardest part)
45 Genetic Algorithms
Benefits of Genetic Algorithms
Concept is easy to understand Modular, separate from application Supports multi-objective optimization Good for “noisy” environments Always has an answer; answer gets
better with time
46 Genetic Algorithms
Benefits of Genetic Algorithms (cont.)
Many ways to speed up and improve a GA-based application as knowledge about problem domain is gained
Easy to exploit previous or alternate solutions
Flexible building blocks for hybrid applications
Substantial history and range of use
47 Genetic Algorithms
When to Use a GA Alternate solutions are too slow or overly
complicated Need an exploratory tool to examine new
approaches Problem is similar to one that has already been
successfully solved by using a GA Want to hybridize with an existing solution Benefits of the GA technology meet key problem
requirements
48 Genetic Algorithms
Some GA Application Types
Domain Application TypesControl gas pipeline, pole balancing, missile evasion, pursuit
Design semiconductor layout, aircraft design, keyboardconfiguration, communication networks
Machine Learning designing neural networks, improving classificationalgorithms, classifier systems
Signal Processing filter design
Game Playing poker, checkers, prisoner’s dilemma
CombinatorialOptimization
set covering, travelling salesman, routing, bin packing,graph colouring and partitioning
49 Genetic Algorithms
Conclusions
Question: ‘If GAs are so smart, why ain’t they rich?’
Answer: ‘Genetic algorithms are rich - rich in application across a large and growing number of disciplines.’- David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning