Memetic Computation: The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era

109 Pages·2019·4.106 MB·English

by  Abhishek Gupta, Yew-Soon Ong

Adaptation, Learning, and Optimization 21
Abhishek Gupta
Yew-Soon Ong
Memetic
Computation
The Mainspring of Knowledge Transfer
in a Data-Driven Optimization Era
Adaptation, Learning, and Optimization
Volume 21
Series editors
Yew-Soon Ong, Nanyang Technological University, Singapore, Singapore
e-mail: [email protected]
Meng-Hiot Lim, Nanyang Technological University, Singapore, Singapore
e-mail: [email protected]
The role of adaptation, learning and optimization are becoming increasingly
essential and intertwined. The capability of a system to adapt either through
modification of its physiological structure or via some revalidation process of
internalmechanismsthatdirectlydictatetheresponseorbehavioriscrucialinmany
real world applications. Optimization lies at the heart of most machine learning
approaches while learning and optimization are two primary means to effect
adaptation in various forms. They usually involve computational processes
incorporated within the system that trigger parametric updating and knowledge
or model enhancement, giving rise to progressive improvement. This book series
serves as a channel to consolidate work related to topics linked to adaptation,
learning and optimization in systems and structures. Topics covered under this
series include:
(cid:129) complex adaptive systems including evolutionary computation, memetic com-
puting, swarm intelligence, neural networks, fuzzy systems, tabu search, sim-
ulated annealing, etc.
(cid:129) machine learning, data mining & mathematical programming
(cid:129) hybridization of techniques that span across artificial intelligence and compu-
tational intelligence for synergistic alliance of strategies for problem-solving.
(cid:129) aspects of adaptation in robotics
(cid:129) agent-based computing
(cid:129) autonomic/pervasive computing
(cid:129) dynamic optimization/learning in noisy and uncertain environment
(cid:129) systemic alliance of stochastic and conventional search techniques
(cid:129) all aspects of adaptations in man-machine systems.
This book series bridges the dichotomy of modern and conventional mathematical
and heuristic/meta-heuristics approaches to bring about effective adaptation,
learningandoptimization.Itpropelsthemaximthattheoldandthenewcancome
together and be combined synergistically to scale new heights in problem-solving.
Toreachsuchalevel,numerousresearchissueswillemergeandresearcherswillfind
thebookseriesa convenientmediumto track the progresses made.
More information about this series at http://www.springer.com/series/8335
Abhishek Gupta Yew-Soon Ong
(cid:129)
Memetic Computation
The Mainspring of Knowledge Transfer
in a Data-Driven Optimization Era
123
Abhishek Gupta Yew-Soon Ong
Schoolof Computer Science Schoolof Computer Science
andEngineering andEngineering
NanyangTechnological University NanyangTechnological University
Singapore, Singapore Singapore, Singapore
ISSN 1867-4534 ISSN 1867-4542 (electronic)
Adaptation, Learning,andOptimization
ISBN978-3-030-02728-5 ISBN978-3-030-02729-2 (eBook)
https://doi.org/10.1007/978-3-030-02729-2
LibraryofCongressControlNumber:2018958923
©SpringerNatureSwitzerlandAG2019
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Preface
Beginningwithsingle-celledorganismsthatcameintoexistencebillionsofyearsago,
theguidingprinciplesofevolutionhavegraduallyshapedecosystemscharacterizedby
astonishingbiodiversity.Whatismore,themechanismofnaturalselection(orsimply,
survival of the fittest) has endowed living organisms with consummate problem-
solvingability;asawaytocopeandadapttothemanychallengesthatmaybeposedby
thenaturalworld.However,biologicalevolutionisanextremelyslowprocess,espe-
cially when viewed against the incredible speed of technological evolution in the
present-day. Notably, while it took millions of years for multicellular organisms to
evolvefrom(single-celled)bacteria,humancivilizationhasbeenabletoprogressfrom
the world’s first land-based mechanical vehicles to supersonic jets, spacecraft,
unmanned drones, etc., within a span of merely three centuries. Such radical trans-
formationofsocietythroughtechnicaldevelopmentisgovernedbyprocessessimilar
tothatofevolution,albeitoccurringinacompletelydifferentspace—namelythatof
scientificknowledge,culture,andideasthatexistinourbrainsandcanbedisseminated
almostinstantly(atleastincomparisontotherateofdisseminationofgenetictraits)
acrosspopulations.Thoughevolutionarymodelsofculturalinformation(knowledge)
propagation have been studied since the time of Darwin, it was only in Richard
Dawkins’1976book“TheSelfishGene”thatthetopicwasfirstcaptionedbytheterm
memetics—withthebasicunitoftransferrableinformationbeinglabelledasameme.
This is a book about memetic computation (MC)—a novel computational
paradigm that explicitly incorporates the aforementioned notion of memes as
building blocks of knowledge for boosting the performance of artificial evolu-
tionarysystemsinthedomainofsearchandoptimization.Whiletheadvantagesof
exploiting heuristic information to speed up search have been well established for
decades,theuniquenessofMCliesinthefactthattheheuristicsneednolongerbe
manually specified. In its place, by taking a data-driven view of optimization, it
becomes possible to uncover and exploit patterns from the data generated online
during the course of a search, so as to autonomously orchestrate custom search
behaviors on the fly—in turn, paving the path to optimizers with general purpose
problem-solving ability (aka, artificial general intelligence). What is more, akin to
their sociocultural origins, the computationalmanifestations of memes need notbe
v
vi Preface
restricted to a single optimizer (read: “hypothetical brain”), but can be sponta-
neouslypropagatedacrossdistinctoptimizerscateringtodifferenttasks(read:“leap
frombraintobrain”).Weperceivethisoutcomeasakindofmachinethinkingthat,
atleastinprinciple,promisestotakethehumanoutoftheloopofalgorithmdesign.
It must be emphasized that the motivation behind writing this book is not to
comeupwithyetanothernature-inspiredglobaloptimizationalgorithm.Wefurther
clarify that neither is our exposition limited to the existing (rather narrow) inter-
pretation of memetic algorithms as a hybridization of manually specified local
search heuristics with some base evolutionary optimizer. In contrast, our goal isto
shed light on the comprehensive realization of MC as a simultaneous problem
learning and optimization paradigm that can potentially showcase human-like
problem-solving prowess. To this end, we describe a series of data-driven
approaches that enable optimization engines to acquire increasing levels of intel-
ligence over time, primarily through the adaptive integration of diverse (compu-
tationally encoded) memes accumulated with experience and/or via interactions
with other engines/systems. In this regard, it is worthmentioning that the practical
deployability ofthemethodsputforwardinthebook isdeemed tobewell aligned
withmoderncomputingplatformslikethecloudandtheInternetofThings—which
promisetofacilitate large-scaledata storage and seamless communication between
machines. With the widespread adoption of these technologies, it is believed that
the salient features of MC are primed to take center stage in the optimization
engines of the future.
Thebookisdividedintotwoparts.Forreadersinterestedinthehistoryandrise
of memetics in computing, a general overview of the so-called first generation of
meme-inspired optimization algorithms is provided in Part I (comprising Chaps. 2
and 3). Chapter 3, in particular, offers a first glimpse of data-driven adaptations in
optimization, especially pertaining to the automatic integration of human crafted
localsearchheuristics.Ontheotherhand,forthosereaderswhoareonlyinterested
inthelatestideasinMC,weencouragethemtoskipaheadtoPartII—whichcanbe
read andunderstood independentlyfrom thefirst.Over Chaps.4–7,theconcept of
memes is set free from the narrow scope of hybrid (global + local search) algo-
rithms, and takes flight to embody potentially diverse forms of fully machine
uncovered problem-solving knowledge.
Throughoutourdiscussions,wemakeitapointtoconciselypresenttherelevant
theoreticalargumentsthathelpexplainthemethodologicaldevelopments.Assuch,
wehavemadeanefforttokeepthecontentsofthebookaccessibletoanyresearcher
familiar with the classical techniques and terminologies of evolutionary computa-
tion. Yet, there are certain subject matters that had to be included that may be
considered uncommon to the field. Unfortunately, a detailed exposition of all the
necessary ingredients, from the ground up, proved to be too extensive for the
compact book that we set out to write. Thus, from Chap. 3 onwards, a prior
(undergraduatelevel)readingofprobability,statistics,andbasicmachinelearningis
recommended for a full appreciation of the mathematical formalizations and algo-
rithmic descriptions. Furthermore, prior know-how about surrogate-assisted/
Bayesian optimization techniques isdeemed helpful,albeit notessential.
Preface vii
Finally, before we set sail, there is indeed a long list of people who must be
acknowledged for having influenced our work, either directly or indirectly, during
and prior to the period the book was written. However, a comprehensive recol-
lectionofallthenamesispracticallyimpossible.Therefore,justtokeepitshort,we
take this opportunity to extend our sincere gratitude to those whose research or
adviceisimmediatelyreflectedinthepagesofthebook.Theyare:Dr.LiangFeng,
Dr. Ramon Sagarna, and doctoral students (at the time of writing) Bingshui Da,
Kavitesh Bali, Xinghua Qu, and Alan Tan Wei Min.
Singapore Abhishek Gupta
Yew-Soon Ong
Contents
1 Introduction: The Rise of Memetics in Computing. . . . . . . . . . . . . . 1
1.1 Simulating Evolution for Search and Optimization . . . . . . . . . . . . 3
1.1.1 The Achilles Heel of Evolutionary Computation . . . . . . . . 5
1.2 Expert Knowledge, Learning, and Optimization . . . . . . . . . . . . . . 6
1.2.1 Stepping-Stones to Comprehensive Memetic
Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Outline of Chapters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Part I Human Crafted Memes
2 Canonical Memetic Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1 Local Versus Global Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2 Pseudocode of the CMA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.1 Lamarckian Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.2 The Baldwin Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3 Some Numerical Revelations. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3.2 Results and Discussions. . . . . . . . . . . . . . . . . . . . . . . . . . 24
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3 Data-Driven Adaptation in Memetic Algorithms. . . . . . . . . . . . . . . . 27
3.1 Adaptive Meta-Lamarckian Learning . . . . . . . . . . . . . . . . . . . . . . 28
3.1.1 Sub-problem Decomposition. . . . . . . . . . . . . . . . . . . . . . . 29
3.1.2 Reward-Proportionate Roulette Wheel Selection . . . . . . . . 31
3.2 The Evolvability Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.1 Statistical Learning of Evolvability. . . . . . . . . . . . . . . . . . 34
3.3 Meme Complexes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.1 Memeplex Representation . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.2 Learning the Memeplex Network Weights. . . . . . . . . . . . . 37
ix
x Contents
3.4 Multi-surrogates in Expensive Global Optimization . . . . . . . . . . . 37
3.4.1 Mixture of Experts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Part II Machine Crafting Memes
4 The Memetic Automaton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.1 Multi-problems: A New Optimization Scenario . . . . . . . . . . . . . . 48
4.1.1 Qualitative Feasibility Assessment of Meme Transfer . . . . 50
4.1.2 The Importance of Search Space Unification. . . . . . . . . . . 52
4.2 A Probabilistic Formalization of Memes . . . . . . . . . . . . . . . . . . . 55
4.2.1 The Effect of a Large and Diverse Knowledge Base . . . . . 56
4.3 Categorizing Multi-problem Settings . . . . . . . . . . . . . . . . . . . . . . 59
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5 Sequential Knowledge Transfer Across Problems. . . . . . . . . . . . . . . 63
5.1 A Brief Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.2 Overview of Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.3 Meme Integration Via Mixture Modeling. . . . . . . . . . . . . . . . . . . 67
5.3.1 Learning Optimal Model Aggregation. . . . . . . . . . . . . . . . 68
5.3.2 Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.4 An Adaptive Memetic Transfer Optimizer . . . . . . . . . . . . . . . . . . 72
5.5 Numerical Experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.5.1 A Toy Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.5.2 A Practical Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.6 Knowledge Transfer in Expensive Optimization . . . . . . . . . . . . . . 78
5.6.1 Mixture Modeling for Regression Transfer . . . . . . . . . . . . 78
5.6.2 A Study in Engineering Design . . . . . . . . . . . . . . . . . . . . 80
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6 Multitask Knowledge Transfer Across Problems . . . . . . . . . . . . . . . 83
6.1 A Brief Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2 Overview of Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.3 An Adaptive Memetic Multitask Optimizer . . . . . . . . . . . . . . . . . 86
6.4 Numerical Experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6.4.1 A Toy Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.4.2 A Practical Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . 90
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
7 Future Direction: Compressed Meme Space Evolutions . . . . . . . . . . 93
7.1 Classification-Based Discrete Optimization. . . . . . . . . . . . . . . . . . 94
7.2 Neural Network-Based Compressed Representation . . . . . . . . . . . 95
7.2.1 Application to Knapsack Problems . . . . . . . . . . . . . . . . . . 97