We model in detail the distribution of Boolean functions implemented by random non-recursive programs, similar to linear genetic programming. Most functions are constants, the remainder are mostly simple. Bounds on how long programs need to be before the distribution of their functionality is close to its limiting distribution are provided in general and for average computers
We contribute to the theoretical understanding of randomized search heuristics by investigating thei...
AbstractThe No Free Lunch (NFL) theorem due to Wolpert and Macready (IEEE Trans. Evol. Comput. 1(1) ...
A general form of stochastic search is described (random heuristic search), and some of its general ...
We model the distribution of functions implemented by non-recursive programs, similar to linear gene...
AbstractThe average time of computing Boolean functions by straight-line programs with random number...
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain conv...
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain conv...
We show that simple mutation-only evolutionary algorithms find a satisfying assignment on two simila...
Genetic algorithms are stochastic search procedures based on randomized operators such as crossover ...
UnrestrictedAn algorithm can be defined as a set of computational steps that transform the input to ...
Randomness is a crucial component in the design and analysis of many efficient algorithms. This thes...
Abstract. Evolutionary Algorithms (EAs) are successfully applied for optimization in discrete search...
The No Free Lunch (NFL) theorem due to Wolpert and Macready (1997) has led to controversial discussi...
This thesis is concerned with the probabilistic analysis of random combinatorial struc-tures and the...
AbstractGenetic algorithms are stochastic search procedures based on randomized operators such as cr...
We contribute to the theoretical understanding of randomized search heuristics by investigating thei...
AbstractThe No Free Lunch (NFL) theorem due to Wolpert and Macready (IEEE Trans. Evol. Comput. 1(1) ...
A general form of stochastic search is described (random heuristic search), and some of its general ...
We model the distribution of functions implemented by non-recursive programs, similar to linear gene...
AbstractThe average time of computing Boolean functions by straight-line programs with random number...
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain conv...
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain conv...
We show that simple mutation-only evolutionary algorithms find a satisfying assignment on two simila...
Genetic algorithms are stochastic search procedures based on randomized operators such as crossover ...
UnrestrictedAn algorithm can be defined as a set of computational steps that transform the input to ...
Randomness is a crucial component in the design and analysis of many efficient algorithms. This thes...
Abstract. Evolutionary Algorithms (EAs) are successfully applied for optimization in discrete search...
The No Free Lunch (NFL) theorem due to Wolpert and Macready (1997) has led to controversial discussi...
This thesis is concerned with the probabilistic analysis of random combinatorial struc-tures and the...
AbstractGenetic algorithms are stochastic search procedures based on randomized operators such as cr...
We contribute to the theoretical understanding of randomized search heuristics by investigating thei...
AbstractThe No Free Lunch (NFL) theorem due to Wolpert and Macready (IEEE Trans. Evol. Comput. 1(1) ...
A general form of stochastic search is described (random heuristic search), and some of its general ...