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26/04/05DMI - Università di Catania 1
Combinatorial Landscapes
Giuseppe Nicosia
University of Catania
Department of Mathematics and Computer Science
www.dmi.unict.it/~nicosia
26/04/05DMI - Università di Catania 1
1. Combinatorial LandscapesThe notion of landscape is among the rare existing concepts which help to understand the behaviour of search algorithmsthe behaviour of search algorithms and heuristics and to characterize the difficultyto characterize the difficulty of a combinatorial problem.
26/04/05DMI - Università di Catania 1
Search SpaceGiven a combinatorial problem PP, a search search spacespace associated to a mathematical formulation of PP is defined by a couple (S,f)(S,f)
– where SS is a finite set of configurations (or nodes or points) and
– ff a cost functioncost function which associates a real number to each configurations of SS.
For this structure two most common measures are the minimum and the maximum coststhe minimum and the maximum costs.In this case we have the combinatorial combinatorial optimization problemsoptimization problems.
26/04/05DMI - Università di Catania 1
Example: K-SATAn instance of the K-SAT problem consists of a set V of variables, a collection C of clauses over V such that each clause c C has |c|= K.
The problem is to find a satisfying truth assignment for C.
The search space for the 2-SAT with |V|=2 is (S,f) where
– SS={ (T,T), (T,F), (F,T), (F,F) } and
– the cost functionthe cost function for 2-SAT computes only the number of satisfied clauses
ffsatsat (s)= #SatisfiedClauses(F,s), s (s)= #SatisfiedClauses(F,s), s S S
26/04/05DMI - Università di Catania 1
An example of Search Space
Let we consider F = (A B) ( A B)
1F T
2F F
2T F
1T Tfsat(F,s) A B
26/04/05DMI - Università di Catania 1
Search Landscape• Given a search space (S,f)(S,f), a search search
landscapelandscape is defined by a triplet (S,n,f)(S,n,f) where nn is a neighborhood functionneighborhood function which verifies
n : S n : S 2 2SS -{ 0} -{ 0}
• This landscape, also called energy landscapeenergy landscape, can be considered as a neutralneutral one since no search process is involved.
• It can be conveniently viewed as weighted weighted graphgraph G=(S, n , F) where the weights are defined on the nodes, not on the edges.
26/04/05DMI - Università di Catania 1
Example and relevance of Landscape
The search Landscape for the K-SAT problem is a N dimensional hypercubeN dimensional hypercube with
N = number of variables = |V| .
• Combinatorial optimization problems are often hard to solvehard to solve since such problems may have huge and complex search landscapehuge and complex search landscape.
26/04/05DMI - Università di Catania 1
Hypercubes
26/04/05DMI - Università di Catania 1
Solvable & Impossible•The New York Times, July 13, 1999 “Separating Separating Insolvable and DifficultInsolvable and Difficult”.
• B. Selman, R. Zecchina, et al.“Determing computational complexity from characteristic ‘phase transitions’ ”, Nature, Vol. 400, 8 July 1999,
26/04/05DMI - Università di Catania 1
Phase Transition, =4.256=4.256
26/04/05DMI - Università di Catania 1
Characterization of the Landscape in terms of Connected Components
Number of solutions, number of connected components and CCs' cardinality versus for #3$-SAT#3$-SAT problem with n=10n=10 variables.
26/04/05DMI - Università di Catania 1
CC's cardinality at phase transition (3)=4.256(3)=4.256
Number of Solutions, number of connected components and CC's cardinality at phase transition (3)=4.256(3)=4.256 versus number of variables nn for #3-SAT problem#3-SAT problem.
26/04/05DMI - Università di Catania 1
Process LandscapeProcess LandscapeGiven a search landscape (S, n, f), a process process landscapelandscape is defined by a quadruplet (S, n, f, (S, n, f, )) where is a search processsearch process.
• The process landscape represents a particular view of the neutral landscape (S, n, f) seen by a search algorithm.
• Examples of search algorithms:
– Local Search Algorithms.
– Complete Algorithms (e. g. Davis-Putnam algorithm).
– Evolutionary AlgorithmsEvolutionary Algorithms: Genetic Algorithms, Genetic Programming, Evolution Strategies, Evolution Programming, Immune Algorithms.
26/04/05DMI - Università di Catania 1
References• G. Nicosia, V. Cutello, Noisy Channel and Reaction-Diffusion Noisy Channel and Reaction-Diffusion
Systems: Models for Artificial Immune SystemsSystems: Models for Artificial Immune Systems, to appear in Lecture Notes in Computer Science LNCS/LNAI 2003.
• G. Nicosia, V. Cutello, M. Pavone, A Hybrid Immune Algorithm A Hybrid Immune Algorithm with Information Gain for the Graph Coloring Problemwith Information Gain for the Graph Coloring Problem, to appear in Lecture Notes in Computer Science LNCS/LNAI 2003.
• G. Nicosia, V. Cutello, Multiple Learning using Immune Multiple Learning using Immune AlgorithmsAlgorithms, Proceedings of the 4th International Conference on Recent Advances in Soft Computing, RASC 2002, pp. 102-107, Nottingham, UK, 12 -13 December 2002.
• G. Nicosia, V. Cutello, An Immunological approach to An Immunological approach to Combinatorial Optimization ProblemsCombinatorial Optimization Problems,Lecture Notes in Computer Science, LNAI 2527 pp. 361-370, 2002.