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Found 25 papers, 2 papers with code
Run Time Bounds for Integer-Valued OneMax Functions
Furthermore, we show that RLS with step size adaptation achieves an optimization time of $\Theta(n \cdot \log(|a|_1))$.
Analysis of the (1+1) EA on LeadingOnes with Constraints
Understanding how evolutionary algorithms perform on constrained problems has gained increasing attention in recent years.
ELEA -- Build your own Evolutionary Algorithm in your Browser
We provide an open source framework to experiment with evolutionary algorithms which we call "Experimenting and Learning toolkit for Evolutionary Algorithms (ELEA)".
Theoretical Study of Optimizing Rugged Landscapes with the cGA
Estimation of distribution algorithms (EDAs) provide a distribution - based approach for optimization which adapts its probability distribution during the run of the algorithm.
Lower Bounds from Fitness Levels Made Easy
One of the first and easy to use techniques for proving run time bounds for evolutionary algorithms is the so-called method of fitness levels by Wegener.
Learning Languages with Decidable Hypotheses
This so-called $W$-index allows for naming arbitrary computably enumerable languages, with the drawback that even the membership problem is undecidable.
Mapping Monotonic Restrictions in Inductive Inference
In particular, we show that explanatory monotone learners, although known to be strictly stronger, do (almost) preserve the pairwise relation as seen in strongly monotone learning.
Maps for Learning Indexable Classes
We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the languages to be learned.
Normal Forms for (Semantically) Witness-Based Learners in Inductive Inference
Such results are key to understanding the, yet undiscovered, mutual relation between various important learning paradigms when learning behaviourally correctly.
Learning Languages in the Limit from Positive Information with Finitely Many Memory Changes
We investigate learning collections of languages from texts by an inductive inference machine with access to the current datum and a bounded memory in form of states.
Learning Half-Spaces and other Concept Classes in the Limit with Iterative Learners
In order to model an efficient learning paradigm, iterative learning algorithms access data one by one, updating the current hypothesis without regress to past data.
Improved Fixed-Budget Results via Drift Analysis
Fixed-budget theory is concerned with computing or bounding the fitness value achievable by randomized search heuristics within a given budget of fitness function evaluations.
Multiplicative Up-Drift
Drift analysis aims at translating the expected progress of an evolutionary algorithm (or more generally, a random process) into a probabilistic guarantee on its run time (hitting time).
Bounding Bloat in Genetic Programming
While many optimization problems work with a fixed number of decision variables and thus a fixed-length representation of possible solutions, genetic programming (GP) works on variable-length representations.
Ring Migration Topology Helps Bypassing Local Optima
We show that, while the (1+1) EA gets stuck in a bad local optimum and incurs a run time of $\Theta(n^{2r})$ fitness evaluations on FORK, island models with a complete topology can achieve a run time of $\Theta(n^{1. 5r})$ by making use of rare migrations in order to explore the search space more effectively.
Destructiveness of Lexicographic Parsimony Pressure and Alleviation by a Concatenation Crossover in Genetic Programming
We show that the Concatenation Crossover GP can efficiently optimize these test functions, while local search cannot be efficient for all three variants independent of employing bloat control.
First-Hitting Times Under Additive Drift
As corollaries, the same is true for our upper bounds in the case of variable and multiplicative drift.
Learning from Informants: Relations between Learning Success Criteria
Learning from positive and negative information, so-called \emph{informants}, being one of the models for human and machine learning introduced by E.~M.~Gold, is investigated.
A Generic Bet-and-run Strategy for Speeding Up Traveling Salesperson and Minimum Vertex Cover
A common strategy for improving optimization algorithms is to restart the algorithm when it is believed to be trapped in an inferior part of the search space.
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 $\Theta(n^k)$.
The Right Mutation Strength for Multi-Valued Decision Variables
The most common representation in evolutionary computation are bit strings.
Solving Problems with Unknown Solution Length at (Almost) No Extra Cost
For their setting, in which the solution length is sampled from a geometric distribution, we provide mutation rates that yield an expected optimization time that is of the same order as that of the (1+1) EA knowing the solution length.
The Benefit of Sex in Noisy Evolutionary Search
For this, we model sexual recombination with a simple estimation of distribution algorithm called the Compact Genetic Algorithm (cGA), which we compare with the classical $\mu+1$ EA.
A Map of Update Constraints in Inductive Inference
We investigate how different learning restrictions reduce learning power and how the different restrictions relate to one another.
Unbiased Black-Box Complexities of Jump Functions
We analyze the unbiased black-box complexity of jump functions with small, medium, and large sizes of the fitness plateau surrounding the optimal solution.
