Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Lehre, Per Kristian"'
Autor:
Benford, Alistair, Lehre, Per Kristian
Due to their complex dynamics, combinatorial games are a key test case and application for algorithms that train game playing agents. Among those algorithms that train using self-play are coevolutionary algorithms (CoEAs). CoEAs evolve a population o
Externí odkaz:
http://arxiv.org/abs/2409.04177
Autor:
Lehre, Per Kristian, Lin, Shishen
Co-evolutionary algorithms (CoEAs), which pair candidate designs with test cases, are frequently used in adversarial optimisation, particularly for binary test-based problems where designs and tests yield binary outcomes. The effectiveness of designs
Externí odkaz:
http://arxiv.org/abs/2407.17875
Autor:
Lehre, Per Kristian, Lin, Shishen
Runtime analysis, as a branch of the theory of AI, studies how the number of iterations algorithms take before finding a solution (its runtime) depends on the design of the algorithm and the problem structure. Drift analysis is a state-of-the-art too
Externí odkaz:
http://arxiv.org/abs/2405.04480
Elitism, which constructs the new population by preserving best solutions out of the old population and newly-generated solutions, has been a default way for population update since its introduction into multi-objective evolutionary algorithms (MOEAs
Externí odkaz:
http://arxiv.org/abs/2305.16870
Autor:
Lehre, Per Kristian
Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applications are often limited by patho
Externí odkaz:
http://arxiv.org/abs/2206.15238
Autor:
Case, Brendan, Lehre, Per Kristian
A key challenge to make effective use of evolutionary algorithms is to choose appropriate settings for their parameters. However, the appropriate parameter setting generally depends on the structure of the optimisation problem, which is often unknown
Externí odkaz:
http://arxiv.org/abs/2004.00327
This paper extends the runtime analysis of non-elitist evolutionary algorithms (EAs) with fitness-proportionate selection from the simple OneMax function to the linear functions. Not only does our analysis cover a larger class of fitness functions, i
Externí odkaz:
http://arxiv.org/abs/1908.08686
We introduce a new benchmark problem called Deceptive Leading Blocks (DLB) to rigorously study the runtime of the Univariate Marginal Distribution Algorithm (UMDA) in the presence of epistasis and deception. We show that simple Evolutionary Algorithm
Externí odkaz:
http://arxiv.org/abs/1907.12438
We perform a rigorous runtime analysis for the Univariate Marginal Distribution Algorithm on the LeadingOnes function, a well-known benchmark function in the theory community of evolutionary computation with a high correlation between decision variab
Externí odkaz:
http://arxiv.org/abs/1904.09239
Autor:
Lehre, Per Kristian, Sudholt, Dirk
We propose a new black-box complexity model for search algorithms evaluating $\lambda$ search points in parallel. The parallel unary unbiased black-box complexity gives lower bounds on the number of function evaluations every parallel unary unbiased
Externí odkaz:
http://arxiv.org/abs/1902.00107