Representing Fitness Landscapes by Valued Constraints to Understand the Complexity of Local Search

Artem Kaznatcheev, David Cohen, Peter Jeavons

Research output: Contribution to journalArticlepeer-review


Local search is widely used to solve combinatorial optimisation problems and to model biological evolution, but the performance of local search algorithms on different kinds of fitness landscapes is poorly understood. Here we consider how fitness landscapes can be represented using valued constraints, and investigate what the structure of such representations reveals about the complexity of local search.First, we show that for fitness landscapes representable by binary Boolean valued constraints there is a minimal necessary constraint graph that can be easily computed. Second,we consider landscapes as equivalent if they allow the same (improving) local search moves;we show that a minimal constraint graph still exists, but is NP-hard to compute.We then develop several techniques to bound the length of any sequence of local search moves. We show that such a bound can be obtained from the numerical values of the constraints in the representation, and show how this bound may be tightened by considering equivalent representations. In the binary Boolean case, we prove that a degree 2 or tree-structured constraint graph gives a quadratic bound on the number of improving moves made by any local search; hence, any landscape that can be represented by such a model will be tractable for any form of local search.Finally, we build two families of examples to show that the conditions in our tractability results are essential. With domain size three, even just a path of binary constraints can model a landscape with an exponentially long sequence of improving moves. With a treewidth-two constraint graph, even with a maximum degree of three, binary Boolean constraints can model a landscape with an exponentially long sequence of improving moves.
Original languageEnglish
Article number12156
Pages (from-to)1077-1102
Number of pages26
JournalJournal of Artificial Intelligence Research
Publication statusPublished - 29 Nov 2020

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