Memetic Algorithms
Dr. N. Krasnogor
Interdisciplinary Optimisation Laboratory
Automated Scheduling, Optimisation and Planning Research Group
School of Computer Science & Information Technology
University of Nottingham
www.cs.nott.ac.uk/~nxk
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Based on:
M. Tabacman, J. Bacardit, I. Loiseau, and N. Krasnogor. Learning classifier systems in optimisation problems: a case
study on fractal travelling salesman problems. In Proceedings of the International Workshop on Learning Classifier
Systems, volume (to appear) of Lecture Notes in Computer Science. Springer, 2008
W.E. Hart, N. Krasnogor, and J.E. Smith, editors. Recent advances in memetic algorithms, volume 166 of Studies in
Fuzzyness and Soft Computing. Springer Berlin Heidelberg New York, 2004. ISBN 3-540-22904-3.
N. Krasnogor. Handbook of Natural Computation, chapter Memetic Algorithms, page (to appear). Natural Computing.
Springer Berlin / Heidelberg, 2009.
N. Krasnogor and J.E. Smith. A tutorial for competent memetic algorithms: model, taxonomy and design issues. IEEE
Transactions on Evolutionary Computation, 9(5):474- 488, 2005
J. Bacardit and N. Krasnogor. Performance and efficiency of memetic pittsburgh learning classifier systems.
Evolutionary Computation, 17(3):(to appear), 2009.
Q.H. Quang, Y.S. Ong, M.H. Lim, and N. Krasnogor. Adaptive cellular memetic algorithm. Evolutionary
Computation, 17(3):(to appear), 2009.
N. Krasnogor and J.E. Smith. Memetic algorithms: The polynomial local search complexity theory perspective.
Journal of Mathematical Modelling and Algorithms, 7:3-24, 2008.
All material available at www.cs.nott.ac.uk/~nxk/publications.html
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Outline of the Talk
– Evolutionary Algorithms Revisited
– MAs’ Motivation (General Versus Problem Specific
Solvers)
– MAs design issues
– A Few Exemplar Applications
– Related Methods and Advanced Topics
– Putting it all Together
– Conclusions, Q&A
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Evolutionary Computation Most
Important Metaphors
Evolution Problem Solving
Environment Problem
Individual Candidate/Feasible Solution
Fitness Solution quality, i.e. objective value
Natural Selection Simulated Pruning of bad solutions
Fitness → survival and reproduction likelihood
Objective value → chances of generating related (but
not necessarily identical) solutions
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Simulated Evolution Consists of:
• The population contains a diverse set of individuals (i.e. solutions
to an optimisation problem)
• Those features that make solutions good under a specific
objective function tend to proliferate
• Variation is introduced into the population by means of mutation,
crossover and, for MAs, local search.
• Selection culls the population throwing out bad solutions and
keeping the most promising ones
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

The Metaphor of “Adaptive
landscape” (Wright, 1932) (1)
• Solutions (i.e. individuals) are represented by n properties + its
quality value.
• One can imagine the space of all solutions and their quality values
represented as a graph (i.e. landscape) in n+1 dimensions.
• In this metaphor, a specific individual is seen as a point in the
landscape.
• Each point in the landscape will have neighbouring points, which
are those solutions that are somehow related to that point implying
a neighbourhood.
• It is called “adaptive” landscape because the quality value function,
F(), depends on the features and properties of the individual and
hence the better those are the higher(smaller) the value.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

An example: Maximisation Problem with HillClimber (2)
Objective Value
a solutions
Solutions have 2 features
Prop 2
Prop 1
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

An example: Maximisation Problem with EA (3)
Prop 2
Prop 1
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Evolutionary Algorithms in Context
• There are several opinions about the use of metaheuristics in
optimisation
• For the majority of problems a specific algorithm could:
– work better than a generic algorithm in a large set of instances ,
– but it can be very limited on a different domain.
– don’t work too good for some instances.
• One important research challenge is :
– to build frameworks that can be robust across a set of problems
delivering good enough/cheap enough/soon enough solutions.
– to a variety of problems and instances.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Outline of the Talk
– Evolutionary Algorithms Revisited
– MAs’ Motivation (General Versus Problem Specific
Solvers)
– MAs design issues
– A Few Exemplar Applications
– Related Methods and Advanced Topics
– Putting it all Together
– Conclusions, Q&A
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

EAs as problem solvers: The pre-90s view
the Rollroyce
The For T for problem for problem P
method performance on problems
solving specific method
metaheuristic method
random search
P
scale of all problems
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Evolutionary Algorithms and domain knowledge
• Fashionable after the 90’s:
to add problem specific information into the EAs by
means of specialized crossover, mutation,
representations and local search
• Result: The performance curve deforms and
– makes EAs better in some problems,
– worst on other problems
– amount of problem specific is varied.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Michalewicz’s Interpretation
EA 4
method performance on problems
EA 2
EA 3
EA 1
P
scale of all problems
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

So What Are Memetic Algorithms?
Adding Domain Knowledge to EAs
Memetic Algorithms (MAs) were originally inspired by:
• Models of adaptation in natural systems that combine evolutionary adaptation of
population of individuals (GAs)
WITH
• Individual learning (LS) within a lifetime (others consider the LS as development
stage). Learning took the form of (problem specific) local search
PLUS
• R. Dawkin’s concept of a meme which represents a unit of cultural evolution that
can exhibit refinement, hence the local search can be adaptive.
BUT WHY?
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

An example: Maximisation Problem with EA (3)
Prop 2
Prop 1
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

The Canonical MA At design
From Eiben’s & Smith “Introduction To Evolutionary Computation”
time lots of
issues arise
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Motivation
There are several reasons why it is worthwhile hybridizing:
• Complex problems can be partially decomposable, different subproblems be better solved by
different methods:
• EA could be used as pre/post processors
• Subproblem specific information can be placed into variation operators or into local searchers
• In some cases there are exact/approximate methods for subproblems
• Well established theoretical results: generally good black-box optimisers do not exist. This is
why successful EAs are usually found in “hybridized form” with domain knowledge added in
• EA are good at exploring the search space but find it difficult to zoom-in good solutions
• Problems have constraints associated to solutions and heuristics/local search are used to repair
solutions found by the EA
• If heuristic/local search strategies in MAs are “first class citizens” then one can raise the level
of generality of the algorithm without sacrificing performance by letting the MA choose which
local search to use.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

A conservation of competence principle applies: the better one
algorithm is solving one specific instance (class) the worst it is solving
a different instance (class) [Wolpert et.al.]
It cannot be expected that a black-box metaheuristic will suit all
problem classes and instances all the time, that is, it is theoretically
impossible to have both ready made of-the-shelf general & good
solvers for all problems.
MAs and Hyperheuristics are good algorithmic templates that aid in
the balancing act of successfully & cheaply using general, of-the-
shelf, reusable solvers (EAs) with adds-on instance (class)
specific features.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

What happens inside an MA?
This is my
solution to your
problem
I used
strategy Population of agents
X
When I grow up
I’ll need to decide
whose problem Evolutionary
Memetics Algorithm
solving strategy to
use
Offspring
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Outline of the Talk
– Evolutionary Algorithms Revisited
– MAs’ Motivation (General Versus Problem Specific
Solvers)
– MAs design issues
– A Few Exemplar Applications
– Related Methods and Advanced Topics
– Putting it all Together
– Conclusions, Q&A
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Baldwinism VS Lamarckianism
• Lamarkian
• traits acquired by an individual during its lifetime can
be transmitted to its offspring
• e.g. replace individual with fitter neighbour
• Baldwinian
• traits acquired by individual cannot be transmitted to
its offspring
• e.g. individual receives fitness (but not genotype) of
fitter neighbour
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Baldwin’s “filter”
Raw fitness
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Diversity
The loss of diversity is specially problematic in MAs as the LS tends
to focus excesively in a few good solutions.
If the MA uses LS up to local optimae then it becomes important
to constantly identify new local optimae
If the MA uses partial LS you could still be navigating around the
basins of attractions of a few solutions
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Diversity
There are various ways to improve diversity (assuming that’s what
one wants!):
• if the population is seeded only do so partially.
• instead of applying LS to every individual choose whom to apply it to.
• use variation operators that ensure diversity (assorted)
• in the local search strategy include a diversity weigth
• modify the selection operator to prevent duplicates
• archives
• modify the acceptance criteria in the local search:
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Diversity
The following modified MC exploits solutions (zooms-in) when the
population is diverse. If the population is converged it explores
(zooms-out)
The temperature T of the MC is defined for each generation as:
when population is diverse
T <= 0 
only accepts improvements
A new solution is accepted when:
when population is converged
T goes to infinity 
accepts both better and worst
solutions (explores)
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Operators Choice
The choice of LS/Heuristic is one of the most important steps in
the design of an MA
1. Local searchers induce search landscapes and there has been various attempts to
characterize these. Kallel et.al. and Merz et.al. have shown that the choice of LS can
have dramatic impact on the efficiency and effectiveness of the MA
2. Krasnogor formally proved that to reduce the worst case run time of MAs LS move
operators must induce search graphs complementary (or disjoint) than those of the
crossover and mutation.
3. Krasnogor and Smith have also shown that the optimal choice of LS operator is not
only problem and instance dependent but also dependent on the state of the overall
search carried by the underlying EA
The obvious way to implement 2&3 is to use multiple local searchers within an MA
(multimeme algorithms)
and we will see that the obvious way of including feedback like that suggested by 1 is to
use self-generated multiple local searchers (self-generating MAs aka co-evolving MAs)
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Fitness Landscapes
Thanks to P. Merz!
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Multiple Local Searchers
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Use of Knowledge
The use of knowledge is essential for the success of a search methods
There are essentially two stages when knowledge is used:
• At design time: eg, in the form of local searchers/heuristics, specific variation
operators, initialization biases, etc.
• At run time:
• using tabu-like mechanisms to avoid revisiting points (explicit)
• using adaptive operators that bias search towards unseen/promising regions of
search space (implicit)
• creating new operators on-the-fly, eg., self-generating or co-evolving MAs
(implicit)
With appropriate data-mining techniques we can turn implicit
knowledge into explicit and feed it back into the design process!
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Specific Considerations for Continuous Domains
There are several factors which makes CD optimisation difficult:
• Different scales might be required for local/global searches
• It is not always possible to determine when a solution is locally
optimal
• Long local searchers might be needed to ensure convergence to
good optima
• Several local searchers exists but they are general methods so they
violate the conservation of competence principle.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Specific Considerations for Continuous Domains
The design of CD MAs can be different than the one needed for
DD.
As there is a need to both do long local searchers and balance it
with global search then:
• LS is truncated after a number of fitness evaluations
• LS is applied sporadically
• But these strategies makes it difficult to guarantee
convergence
To the best of my knowledge the only MAs for CD that have
guaranteed convergence to LO are Hart’s Memetic Evolutionary
Pattern Search.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Initialisation
Intelligent initialisation of the MA is one of the obvious ways of
reusing knowledge:
• One does not reinvent the wheel ‘cos existing solutions are reused.
• Bias the search mechanism towards more suitable regions of the
search space.
• Given a CPU budget allocation it might pay to spend some part of
the budget in smart initialisations rather than in a pure EA.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

F F: fitness after a smart initialization
Fitness
T: time needed by an EA with random initialization to
reach F
T T T
Time
T ,T Time needed by the Intelligent but remember
Initialization diversity!
If T < T then it is worth initializing.
If T < T then it is not worth doing it
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Memetic Algorithms: the issues involved
Other Hybridisations
EA + LS have been used in various other hybridisation schemes:
• during the genotype to phenotype mapping prior to evaluation, e.g.
in timetabling, scheduling and VRP.
• during the mutation or crossover stages, e.g., DPX is a good
example of intelligent crossover, and Unger & Moult used a try-best
approach for protein folding. Note however that these differ from
Xover hill-climbing in that the later does not use problem/instance
specific knowledge
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Outline of the Talk
– Evolutionary Algorithms Revisited
– MAs’ Motivation (General Versus Problem Specific
Solvers)
– MAs design issues
– A Few Exemplar Applications
– Related Methods and Advanced Topics
– Putting it all Together
– Conclusions, Q&A
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
The Maximum Diversity Problem
Katayama & Narihisa solve the MDP by means of a sophisticated
MA. The MDP:
The problem consists in selecting out of a set of N elements, M
which maximize certain diversity measure Dij
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
The Maximum Diversity Problem
This problem is at the core of various important real-world
applications:
• Immigration and admission policies
• Committee formation
• Curriculum design
• Portfolio selection
• Combinatorial chemical libraries
• etc
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
Protein Structure Prediction by us
Primary Structure
Secondary Structure
Tertiary Structure
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
Protein Structure Prediction
Krasnogor, Krasnogor & Smith, Krasnogor & Pelta, Smith have used
MAs to study fundamentals of the algorithmics behind PSP in
simplified models.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
Protein Structure Prediction
Standard MA template except that Multiple Memes which promote
diversity by means of fuzzy rules are used
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
Protein Structure Prediction
Membership function for “acceptable” solutions
Two distinct “acceptability” concepts
Promotes Promotes
improvements Diversity
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
Protein Structure Prediction
New optimal solutions
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
Optimal Engine Calibration
The OEC problem is paradigmatic of many industrial problems.
In this problem many combinatorial optimisation problems occur:
1. Optimal Design of Experiments
2. Optimal Test Bed Schedule
3. Look-up Table Calculation
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
Optimal Engine Calibration
By P.Merz:
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
Optimal Engine Calibration
Standard MA template
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
Circuit Partitioning
CP is the task of dividing a circuit into smaller parts. Its an
important component of the VLSI Layout problem:
1. this division permits the fabrication of circuitsain physically
the is a minimization objective this is constraint
distinct components
2. By dividing we conquer: resulting circuits can fit fabrication
norms, complexity is reduced
3. Can reduce heat dissipation, energy consumption, etc.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
Circuit Partitioning
From S.Areibi’s chapter:
A graphical example
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Showcase Applications
The Maximum Diversity Problem
Various features: distinct repair & LS, GRASP
for init, diversification phase, accelerated LS.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Outline of the Talk
– Evolutionary Algorithms Revisited
– MAs’ Motivation (General Versus Problem Specific
Solvers)
– MAs design issues
– A Few Exemplar Applications
– Related Methods and Advanced Topics
– Putting it all Together
– Conclusions, Q&A
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Related Methodologies
Teams of Heuristics
Variable Neighbourhood Search: under this approach a number of different
neighbourhood structures are systematically explored, tries to improve the current
solution while avoiding poor local optima.
A-teams of Heuristics: in A-Teams a set of constructive, improvement and
destructive heuristics are asynchronously used to improve solutions.
Hyperheuristics: the main concept behind the hyperheuristic is that of managing the
application of other heuristics adaptively with the purpose of improving solutions.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Methodologies
Cooperative Local Search (Landa Silva & Burke)
Cycle of each individual in pop
The search cycle of each
Cooperation mechanism
individual begins
Gets stuck
sharing moves,
parts, centralized
control, etc
Finds something to do. Gets unstuck
Note that this differs from teams of heuristics in that here the
cooperation is made explicit
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Methodologies
On-the-fly operators discovery
All the previous methodologies clearly benefits the end user as
they have been shown to provide improvements in robustness,
quality, etc.
But what do we do if we do not have, or don’t know, good heuristics
which could be used by,eg., A-teams, VNS or CLS?
Also, why don’t we use the information the algorithm produces
to better understand and make explicit new knowledge of the search
landscape capturing this knowledge in new operators?
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Methodologies
On-the-fly operators discovery
Two alternatives:
1. Off-line: Whitley and Watson did it successfully for TS, and
Kallel et al for other methods.
2. In-line: Krasnogor, Krasnogor & Gustafson, J.E. Smith and
others for MAs
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Methodologies
On-the-fly operators discovery
Canonical MA cycle
Adapted from Durham,
W.: Coevolution: Genes, Culture and Human Diversity.
Stanford University Press (1991)
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Methodologies
On-the-fly operators discovery
Self-Generating/Co-evolving Mas
Adapted from Durham,
W.: Coevolution: Genes, Culture and Human Diversity.
Stanford University Press (1991)
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Methodologies
On-the-fly operators discovery
• Inheritance: an agent inherits the
meme of the most successful of its
parents
There are various processes that • Imitation: an agent imitates a
successful non-genetically-related
guide the Agent’s cultural
individual
evolution of local search
strategies:
• Innovation: an agent blindly
(i.e.randomly) change its meme
• Mental Simulation: an agent
purposely (e.g. hill-climbs to ) improve
its meme
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

From Krasnogor & Gustafson chapter
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

From Smith chapter
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Outline of the Talk
– Evolutionary Algorithms Revisited
– MAs’ Motivation (General Versus Problem Specific
Solvers)
– MAs design issues
– A Few Exemplar Applications
– Related Methods and Advanced Topics
– Putting it all Together
– Conclusions, Q&A
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

So What Are Memetic
Algorithms?
o MAs are carefully orchestrated interplay
between (stochastic) global search and
(stochastic) local search algorithms
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

A Pattern Language for Memetic Algorithms
Memetic Algorithms by N. Krasnogor. Handbook of Natural Computation (chapter) in Natural Computing. Springer Berlin / Heidelberg, 2009.
www.cs.nott.ac.uk/~nxk/publications.html
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Outline of the Talk
– Evolutionary Algorithms Revisited
– MAs’ Motivation (General Versus Problem Specific
Solvers)
– MAs design issues
– A Few Exemplar Applications
– Related Methods and Advanced Topics
– Putting it all Together
– Conclusions, Q&A
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Conclusions (I)
• There is much more in MA that meets the eye. Its not a simple
matter of ad-hoc putting LS somewhere in the EA cycle.
• Just a small space of the architectural space of MAs has been
explored and we don’t know yet why a given architecture
performs well/bad in a specific problem (see my PhD thesis)
• People usually use one “silver bullet” LS. That’s fine if that
SB exists. However when it does not exist use multimeme
algorithms, or other heuristics teams/cooperative algorithms as
lots of simple heuristics can synergistically do the trick.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Conclusions (II)
• ADAPT: the search process is dynamic and your method should
detect and adapt to changing circumstances. Adaptation is not too
expensive or complex to code!
• Carefully consider how your variation operators interact with LS
• Consider whether Baldwinian or Lamarckianism is better
• Understand that the fitness landscape explored by your MA is not
a one-operator landscape but the results of the superposicion with
interference of varios landscapes.
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Conclusions (III)
• Use more expresive acceptance criteria in your local search, eg.,
fuzzy criteria
• If you don’t know what operators to apply let the the MA find it
for you by some Self-Generating mechanism, e.g., co-evolution.
• Self-Generating mechanisms are a great niche for GPers!
FINALLY:
check out the literature, almost surely you will find MAs.
among the best success stories in applications to real world probs!
Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel

Ben Gurion University of the Negeve – June 23rd to July 5th 2009
Distinguished Scientist Visitor Program – Beer Sheva, Israel