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Found 21 papers, 0 papers with code
Runtime analysis of a coevolutionary algorithm on impartial combinatorial games
In this paper, we push the scope of runtime analysis to combinatorial games, proving a general upper bound for the number of simulated games needed for UMDA (a type of CoEA) to discover (with high probability) an optimal strategy for an impartial combinatorial game.
Overcoming Binary Adversarial Optimisation with Competitive Coevolution
On the other hand, the standard $(1,\lambda)$-EA fails to find an $\varepsilon$ approximation to the optimal solution of the \Diagonal problem in polynomial runtime.
Concentration Tail-Bound Analysis of Coevolutionary and Bandit Learning Algorithms
Our drift theorem can be used to prove a strong concentration of the runtime/regret of algorithms in AI.
Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results
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) in the late 1990s.
Runtime Analysis of Competitive co-Evolutionary Algorithms for Maximin Optimisation of a Bilinear Function
Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs.
Self-adaptation in non-Elitist Evolutionary Algorithms on Discrete Problems with Unknown Structure
The structure of this problem depends on a parameter $k$, which is \emph{a priori} unknown to the algorithm, and which is needed to appropriately set a fixed mutation rate.
Runtime Analysis of Fitness-Proportionate Selection on Linear Functions
In contrast to this negative result, we also show that for any linear function with polynomially bounded weights, the EA achieves a polynomial expected runtime if the mutation rate is reduced to $\Theta(1/n^2)$ and the population size is sufficiently large.
On the Limitations of the Univariate Marginal Distribution Algorithm to Deception and Where Bivariate EDAs might help
More precisely, we show that the UMDA with a parent population size of $\mu=\Omega(\log n)$ has an expected runtime of $e^{\Omega(\mu)}$ on the DLB problem assuming any selective pressure $\frac{\mu}{\lambda} \geq \frac{14}{1000}$, as opposed to the expected runtime of $\mathcal{O}(n\lambda\log \lambda+n^3)$ for the non-elitist $(\mu,\lambda)~\text{EA}$ with $\mu/\lambda\leq 1/e$.
Runtime Analysis of the Univariate Marginal Distribution Algorithm under Low Selective Pressure and Prior Noise
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 variables.
Parallel Black-Box Complexity with Tail Bounds
Our main result is a general performance limit: we prove that on every function every $\lambda$-parallel unary unbiased algorithm needs at least $\Omega(\frac{\lambda n}{\ln \lambda} + n \log n)$ evaluations to find any desired target set of up to exponential size, with an overwhelming probability.
Level-Based Analysis of the Univariate Marginal Distribution Algorithm
The facility and generality of our arguments suggest that this is a promising approach to derive bounds on the expected optimisation time of EDAs.
Level-Based Analysis of the Population-Based Incremental Learning Algorithm
The Population-Based Incremental Learning (PBIL) algorithm uses a convex combination of the current model and the empirical model to construct the next model, which is then sampled to generate offspring.
Improved Runtime Bounds for the Univariate Marginal Distribution Algorithm via Anti-Concentration
Unlike traditional evolutionary algorithms which produce offspring via genetic operators, Estimation of Distribution Algorithms (EDAs) sample solutions from probabilistic models which are learned from selected individuals.
Theoretical Analysis of Stochastic Search Algorithms
This quickly increasing basis of results allows, nowadays, the analysis of sophisticated algorithms such as population-based evolutionary algorithms, ant colony optimisation and artificial immune systems.
Escaping Local Optima using Crossover with Emergent or Reinforced Diversity
This proves a sizeable advantage of all variants of the ($\mu$+1) GA compared to (1+1) EA, which requires time $\Theta(n^k)$.
Populations can be essential in tracking dynamic optima
It is often claimed that evolutionary algorithms are particularly suitable for dynamic optimisation because a large population can contain different solutions that may be useful in the future.
Self-adaptation of Mutation Rates in Non-elitist Populations
Experimental results indicate that self-adaptation, where parameter settings are encoded in the genomes of individuals, can be effective in continuous optimisation.
Level-Based Analysis of Genetic Algorithms for Combinatorial Optimization
The paper is devoted to upper bounds on run-time of Non-Elitist Genetic Algorithms until some target subset of solutions is visited for the first time.
Level-based Analysis of Genetic Algorithms and other Search Processes
Finally, we prove that the theorem is nearly optimal for the processes considered.
A Parameterized Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms
For the generalised minimum spanning tree problem, we analyse the two approaches presented by Hu and Raidl (2012) with respect to the number of clusters that distinguish each other by the chosen representation of possible solutions.
General Drift Analysis with Tail Bounds
We address this lack by providing a general drift theorem that includes bounds on the upper and lower tail of the hitting time distribution.
