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- Timo Kötzing, Andrei Lissovoi, Carsten Witt
- FOGA
- 2015

Evolutionary algorithms (EAs) perform well in settings involving <i>uncertainty</i>, including settings with stochastic or dynamic fitness functions. In this paper, we analyze the (1+1) EA on dynamically changing OneMax, as introduced by Droste (2003). We re-prove the known results on first hitting times using the modern tool of drift analysis. We extend… (More)

- Andrei Lissovoi, Carsten Witt
- Theor. Comput. Sci.
- 2013

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)

- Andrei Lissovoi, Carsten Witt
- Algorithmica
- 2015

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)

- Andrei Lissovoi, Carsten Witt
- GECCO
- 2015

A simple island model with λ islands and migration occurring after every τ iterations is studied on the dynamic fitness function Maze. This model is equivalent to a (1+λ) EA if τ=1, i.e., migration occurs during every iteration. It is proved that even for an increased offspring population size up to… (More)

- Andrei Lissovoi, Carsten Witt
- GECCO
- 2016

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)

- Andrei Lissovoi, Carsten Witt
- Algorithmica
- 2016

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)

- Andrei Lissovoi, Carsten Witt
- GECCO
- 2014

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)

- Andrei Lissovoi, Carsten Witt
- Algorithmica
- 2017

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)

- Andrei Lissovoi
- 2012

iii Preface This thesis was prepared at the department of Informatics and Mathematical Modelling at the Technical University of Denmark in fulfillment of the requirements for acquiring an M.Sc. in Computer Science and Engineering. It was supervised by associate professor Carsten Witt, and assigned a workload of 30 ECTS credits. The thesis examines how… (More)