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- Arina Buzdalova, Maxim Buzdalov, Vladimir Parfenov
- SSBSE
- 2013

Generation of performance tests for programming challenge tasks is considered. A number of evolutionary approaches are compared on two different solutions of an example problem. It is shown that using helper-objectives enhances evolutionary algorithms in the considered case. The general approach involves automated selection of such objectives.

- Viktor Arkhipov, Maxim Buzdalov, Anatoly Shalyto
- 2013 12th International Conference on Machine…
- 2013

Worst-case execution time tests can be tricky to create for various computer science algorithms. To reduce the amount of human effort, authors suggest using search-based optimization techniques, such as genetic algorithms. This paper addresses difficult test generation for several maximum flow algorithms from the augmenting path family. The presented… (More)

- Maxim Buzdalov
- 2012 11th International Conference on Machine…
- 2012

In this paper, an automated method for generation of tests against inefficient solutions for programming challenge tasks on graph theory is proposed. The method is based on the use of (1+1) evolution strategy and is able to defeat several kinds of inefficient solutions. The proposed method was applied to a task from the Internet problem archive, the Timus… (More)

- Arina Buzdalova, Maxim Buzdalov
- 2012 11th International Conference on Machine…
- 2012

In this paper further investigation of the previously proposed method of speeding up single-objective evolutionary algorithms is done. The method is based on reinforcement learning which is used to choose auxiliary fitness functions. The requirements for this method are formulated. The compliance of the method with these requirements is illustrated on model… (More)

- Maxim Buzdalov, Arina Buzdalova, Anatoly Shalyto
- 2013 12th International Conference on Machine…
- 2013

A first step towards analyzing runtime complexity of an evolutionary algorithm adaptively adjusted using reinforcement learning is made. We analyze the previously proposed EA + RL method that enhances single-objective optimization by selecting efficient auxiliary fitness functions. Precisely, Random Mutation Hill Climber adjusted with Q-learning using… (More)

- Maxim Buzdalov, Ilya Yakupov, Andrey Stankevich
- GECCO
- 2015

Genetic algorithms (GAs) are widely used in multi-objective optimization for solving complex problems. There are two distinct approaches for GA design: generational and steady-state algorithms. Most of the current state-of-the-art GAs are generational, although there is an increasing interest to steady-state algorithms as well. However, for algorithms based… (More)

- Maxim Buzdalov, Vladimir Parfenov
- GECCO
- 2015

Steady-state evolutionary algorithms are often favoured over generational ones due to better scalability in parallel and distributed environments. However, in certain conditions they are able to produce results of better quality as well. We consider several ways to introduce various ``degrees of steadiness'' in the NSGA-II algorithm, some of which have not… (More)

- Maxim Buzdalov, Arina Buzdalova
- GECCO
- 2014

There exist optimization problems with the target objective, which is to be optimized, and several extra objectives, which can be helpful in the optimization process. The previously proposed EA+RL method is designed to adaptively select objectives during the run of an optimization algorithm in order to reduce the number of evaluations needed to reach an… (More)

- Maxim Buzdalov
- GECCO
- 2011

In this paper, an automated method for generation of tests in order to detect inefficient (slow) solutions for programming challenge tasks is proposed. The method is based on genetic algorithms.
The proposed method was applied to a task from the Internet problem archive - the Timus Online Judge. For this problem, none of the existed solutions passed the… (More)

- Maxim Buzdalov, Anatoly Shalyto
- PPSN
- 2014

The non-dominated sorting algorithm by Jensen, generalized by Fortin et al to handle the cases of equal objective values, has the running time complexity of O(N log K−1 N) in the general case. Here N is the number of points, K is the number of objectives and K is thought to be a constant when N varies. However, the complexity was not proven to be the same… (More)