Andrei Lissovoi

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A simple ACO algorithm called λ-MMAS for dynamic variants of the single-destination shortest paths problem is studied by rigorous runtime analyses. Building upon previous results for the special case of 1-MMAS, it is studied to what extent an enlarged colony using $\lambda$ ants per vertex helps in tracking an oscillating optimum. It is shown that easy(More)
We study the behavior of a population-based EA and the Max–Min Ant System (MMAS) on a family of deterministically-changing fitness functions, where, in order to find the global optimum, the algorithms have to find specific local optima within each of a series of phases. In particular, we prove that a (2+1) EA with genotype diversity is able to find the(More)
We introduce a simplified island model with behavior similar to the λ (1+1) islands optimizing the Maze fitness function, and investigate the effects of the migration topology on the ability of the simplified island model to track the optimum of a dynamic fitness function. More specifically, we prove that there exist choices of model parameters for(More)
Selection hyper-heuristics are randomised search methodologies which choose and execute heuristics from a set of low-level heuristics. Recent time complexity analyses for the L<scp>eading</scp>O<scp>nes</scp> benchmark function have shown that the standard simple random, permutation, random gradient, greedy and reinforcement learning selection mechanisms(More)
A simple island model with $$\lambda $$ λ islands and migration occurring after every $$\tau $$ τ iterations is studied on the dynamic fitness function Maze. This model is equivalent to a $$(1+\lambda )$$ ( 1 + λ )  EA if $$\tau =1$$ τ = 1 , i. e., migration occurs during every iteration. It is proved that even for an increased offspring population size up(More)
We study the behavior of a population-based EA and the Max-Min Ant System (MMAS) on a family of deterministically-changing fitness functions, where, in order to find the global optimum, the algorithms have to find specific local optima within each of a series of phases. In particular, we prove that a (2+1) EA with genotype diversity is able to find the(More)
Island models denote a distributed system of evolutionary algorithms which operate independently, but occasionally share their solutions with each other along the so-called migration topology. We investigate the impact of the migration topology by introducing a simplified island model with behavior similar to $$\lambda $$ λ islands optimizing the so-called(More)