A Tight Runtime Analysis for the cGA on Jump Functions---EDAs Can Cross Fitness Valleys at No Extra Cost

We prove that the compact genetic algorithm (cGA) with hypothetical population size $\mu = \Omega(\sqrt n \log n) \cap \text{poly}(n)$ with high probability finds the optimum of any $n$-dimensional jump function with jump size $k < \frac 1 {20} \ln n$ in $O(\mu \sqrt n)$ iterations. Since it is known that the cGA with high probability needs at least $\Omega(\mu \sqrt n + n \log n)$ iterations to optimize the unimodal OneMax function, our result shows that the cGA in contrast to most classic evolutionary algorithms here is able to cross moderate-sized valleys of low fitness at no extra cost. Our runtime guarantee improves over the recent upper bound $O(\mu n^{1.5} \log n)$ valid for $\mu = \Omega(n^{3.5+\varepsilon})$ of Hasen\"ohrl and Sutton (GECCO 2018). For the best choice of the hypothetical population size, this result gives a runtime guarantee of $O(n^{5+\varepsilon})$, whereas ours gives $O(n \log n)$. We also provide a simple general method based on parallel runs that, under mild conditions, (i)~overcomes the need to specify a suitable population size, but gives a performance close to the one stemming from the best-possible population size, and (ii)~transforms EDAs with high-probability performance guarantees into EDAs with similar bounds on the expected runtime.Comment: 25 pages, full version of a paper to appear at GECCO 201

Lazy Parameter Tuning and Control:Choosing All Parameters Randomly from a Power-Law Distribution

Most evolutionary algorithms have multiple parameters and their values drastically affect the performance. Due to the often complicated interplay of the parameters, setting these values right for a particular problem (parameter tuning) is a challenging task. This task becomes even more complicated when the optimal parameter values change significantly during the run of the algorithm since then a dynamic parameter choice (parameter control) is necessary. In this work, we propose a lazy but effective solution, namely choosing all parameter values (where this makes sense) in each iteration randomly from a suitably scaled power-law distribution. To demonstrate the effectiveness of this approach, we perform runtime analyses of the $(1+(\lambda,\lambda))$ genetic algorithm with all three parameters chosen in this manner. We show that this algorithm on the one hand can imitate simple hill-climbers like the $(1+1)$ EA, giving the same asymptotic runtime on problems like OneMax, LeadingOnes, or Minimum Spanning Tree. On the other hand, this algorithm is also very efficient on jump functions, where the best static parameters are very different from those necessary to optimize simple problems. We prove a performance guarantee that is comparable, sometimes even better, than the best performance known for static parameters. We complement our theoretical results with a rigorous empirical study confirming what the asymptotic runtime results suggest.Comment: Extended version of the paper accepted to GECCO 2021, including all the proofs omitted in the conference versio

Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax

The evolutionary diversity optimization aims at finding a diverse set of solutions which satisfy some constraint on their fitness. In the context of multi-objective optimization this constraint can require solutions to be Pareto-optimal. In this paper we study how the GSEMO algorithm with additional diversity-enhancing heuristic optimizes a diversity of its population on a bi-objective benchmark problem OneMinMax, for which all solutions are Pareto-optimal. We provide a rigorous runtime analysis of the last step of the optimization, when the algorithm starts with a population with a second-best diversity, and prove that it finds a population with optimal diversity in expected time $O(n^2)$, when the problem size $n$ is odd. For reaching our goal, we analyse the random walk of the population, which reflects the frequency of changes in the population and their outcomes.Comment: The full version of the paper accepted to FOGA 2023 conferenc

METHODS OF DIAGNOSTIC EFFECTIVENESS ORGANIZATIONAL CHANGES IN CARGO MOTOR TRANSPORTATION ORGANIZATIONS

В статье предложена методика диагностики результативности организационных изменений в автотранспортных организациях сферы грузовых перевозок. Приведены основные результаты апробации разработанной методики диагностики результативности организационных изменений в 37 автотранспортных организациях сферы грузовых перевозок Санкт-Петербурга и Ленинградской области. Построены диаграммы соответствия состояний удельных весов, в результате реализации организационных изменений. Представлены диаграммы результативности организационных изменений в автотранспортных организациях сферы грузовых перевозок на каждой стадии жизненного цикла.Цель статьи – разработка методики диагностики результативности организационных изменений в грузовых автотранспортных организациях.The article propose methods of diagnostic effectiveness organizational changes in cargo motor transportation organizations. The article contain the main results approbation of the developed methods of diagnostic effectiveness organizational changes in cargo motor transportation organizations in 37 cargo motor transportation organizations of Saint-Petersburg and the Leningrad Region. Constructed diagrams of conformity specific weights resulting from organizational changes in cargo motor transportation organizations. Presents diagrams effectiveness organizational changes in cargo motor transportation organizations at every stages of the life cycle.The goal of the present paper is to development methods of diagnostic effectiveness organizational changes in cargo motor transportation organizations

Reducing harmful air emissions from vehicles, ships and stationary facilities operating in the Arctic

The paper proposes a method for reducing harmful emissions into the Arctic atmosphere during the operation of diesel engines using electromagnetic waves of ultra-high frequency (UHF) and ultrasound. Experimental studies were conducted, which confirmed the reduction of NOx emissions by 50%. In order to implement the method, a multipurpose fuel unit is being developed

Fast Mutation in Crossover-based Algorithms

The heavy-tailed mutation operator proposed in Doerr, Le, Makhmara, and Nguyen (GECCO 2017), called \emph{fast mutation} to agree with the previously used language, so far was proven to be advantageous only in mutation-based algorithms. There, it can relieve the algorithm designer from finding the optimal mutation rate and nevertheless obtain a performance close to the one that the optimal mutation rate gives. In this first runtime analysis of a crossover-based algorithm using a heavy-tailed choice of the mutation rate, we show an even stronger impact. For the $(1+(\lambda,\lambda))$ genetic algorithm optimizing the OneMax benchmark function, we show that with a heavy-tailed mutation rate a linear runtime can be achieved. This is asymptotically faster than what can be obtained with any static mutation rate, and is asymptotically equivalent to the runtime of the self-adjusting version of the parameters choice of the $(1+(\lambda,\lambda))$ genetic algorithm. This result is complemented by an empirical study which shows the effectiveness of the fast mutation also on random satisfiable Max-3SAT instances.Comment: This is a version of the same paper presented at GECCO 2020 completed with the proofs which were missing because of the page limi

Precision improvement of MEMS gyros for indoor mobile robots with horizontal motion inspired by methods of TRIZ

In the paper, the problem of precision improvement for the MEMS gyrosensors on indoor robots with horizontal motion is solved by methods of TRIZ ("the theory of inventive problem solving").Comment: 6 pages, the paper is accepted to 9th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, Hawaii, USA (IEEE-NEMS 2014) as an oral presentatio