3,227 research outputs found
A statistical learning based approach for parameter fine-tuning of metaheuristics
Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer ReviewedPostprint (published version
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Evolution of biological cooperation: An algorithmic approach
This manuscript presents an algorithmic approach to cooperation in biological systems, drawing on fundamental ideas from statistical mechanics and probability theory. Fisher’s geometric model of adaptation suggests that the evolution of organisms well adapted to multiple constraints comes at a significant complexity cost. By utilizing combinatorial models of fitness, we demonstrate that the probability of adapting to all constraints decreases exponentially with the number of constraints, thereby generalizing Fisher’s result. Our main focus is understanding how cooperation can overcome this adaptivity barrier. Through these combinatorial models, we demonstrate that when an organism needs to adapt to a multitude of environmental variables, division of labor emerges as the only viable evolutionary strategy
Complexity of evolutionary equilibria in static fitness landscapes
A fitness landscape is a genetic space -- with two genotypes adjacent if they
differ in a single locus -- and a fitness function. Evolutionary dynamics
produce a flow on this landscape from lower fitness to higher; reaching
equilibrium only if a local fitness peak is found. I use computational
complexity to question the common assumption that evolution on static fitness
landscapes can quickly reach a local fitness peak. I do this by showing that
the popular NK model of rugged fitness landscapes is PLS-complete for K >= 2;
the reduction from Weighted 2SAT is a bijection on adaptive walks, so there are
NK fitness landscapes where every adaptive path from some vertices is of
exponential length. Alternatively -- under the standard complexity theoretic
assumption that there are problems in PLS not solvable in polynomial time --
this means that there are no evolutionary dynamics (known, or to be discovered,
and not necessarily following adaptive paths) that can converge to a local
fitness peak on all NK landscapes with K = 2. Applying results from the
analysis of simplex algorithms, I show that there exist single-peaked
landscapes with no reciprocal sign epistasis where the expected length of an
adaptive path following strong selection weak mutation dynamics is
even though an adaptive path to the optimum of length less
than n is available from every vertex. The technical results are written to be
accessible to mathematical biologists without a computer science background,
and the biological literature is summarized for the convenience of
non-biologists with the aim to open a constructive dialogue between the two
disciplines.Comment: 14 pages, 3 figure
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
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