Results 1 
8 of
8
Large populations are not always the best choice in genetic programming
 GECCO99. Proceedings of the Genetic and Evolutionary ComputationConference
, 1999
"... In genetic programming a general consensus is that the population should be as large as practically possible or sensible. In this paper we examine a batch of problems of combinatory logic, previously successfully tackled with genetic programming, which seemto defy this consensus. Our experimental da ..."
Abstract

Cited by 7 (1 self)
 Add to MetaCart
(Show Context)
In genetic programming a general consensus is that the population should be as large as practically possible or sensible. In this paper we examine a batch of problems of combinatory logic, previously successfully tackled with genetic programming, which seemto defy this consensus. Our experimental data gives evidence that smaller populations are competitive or even slightly better. Moreover, hillclimbing appears to exhibit the best performance. While these results are in a way unexpected, theoretical considerations provide a possible explanation in terms of a special constellation rather than a general misconception as to the bene ts of large populations or genetic programming as such. 1
EURASIP Journal on Applied Signal Processing 2005:1, 99–111 c ○ 2005 Hindawi Publishing Corporation Subband Array Implementations for SpaceTime Adaptive Processing
, 2004
"... Intersymbol interference (ISI) and cochannel interference (CCI) are two primary sources of signal impairment in mobile communications. In order to suppress both ISI and CCI, spacetime adaptive processing (STAP) has been shown to be effective in performing spatiotemporal equalization, leading to in ..."
Abstract
 Add to MetaCart
(Show Context)
Intersymbol interference (ISI) and cochannel interference (CCI) are two primary sources of signal impairment in mobile communications. In order to suppress both ISI and CCI, spacetime adaptive processing (STAP) has been shown to be effective in performing spatiotemporal equalization, leading to increased communication capacity as well as improved quality of service. The high complexity and slow convergence, however, often impede practical STAP implementations. Several subband array structures have been proposed as alternatives to STAP. These structures provide optimal or suboptimal steadystate performance with reduced implementation complexity and improved convergence performance. The purpose of this paper is to investigate the steadystate performance of subband arrays with centralized and localized feedback schemes, using different decimation rates. Analytical expressions of the minimum meansquare error (MMSE) performance are derived. The analysis assumes discrete Fourier transform (DFT)based subband arrays and considers both unconstrained and constrained weight adaptations.
Fitness distributions in evolutionary computation: motivation and examples in the continuous domain
, 1999
"... Evolutionary algorithms are, fundamentally, stochastic search procedures. Each next population is a probabilistic function of the current population. Various controls are available to adjust the probability mass function that is used to sample the space of candidate solutions at each generation. For ..."
Abstract
 Add to MetaCart
(Show Context)
Evolutionary algorithms are, fundamentally, stochastic search procedures. Each next population is a probabilistic function of the current population. Various controls are available to adjust the probability mass function that is used to sample the space of candidate solutions at each generation. For example, the step size of a singleparent variation operator can be adjusted with a corresponding effect on the probability of finding improved solutions and the expected improvement that will be obtained. Examining these statistics as a function of the step size leads to a ‘fitness distribution’, a function that trades off the expected improvement at each iteration for the probability of that improvement. This paper analyzes the effects of adjusting the step size of Gaussian and Cauchy mutations, as well as a mutation that is a convolution of these two distributions. The results indicate that fitness distributions can be effective in identifying suitable parameter settings for these operators. Some comments on the utility of extending this protocol toward the general diagnosis of evolutionary algorithms is also offered. © 1999 Elsevier Science Ireland Ltd. All rights reserved.
n.n.
"... This chapter addresses the question “what is a building block in genetic programming? ” by examining the smallest subtree possible—a single leaf node. The analysis of these subtrees indicates a considerably more complex portrait of what exactly is meant by a building block in GP than what has tradit ..."
Abstract
 Add to MetaCart
(Show Context)
This chapter addresses the question “what is a building block in genetic programming? ” by examining the smallest subtree possible—a single leaf node. The analysis of these subtrees indicates a considerably more complex portrait of what exactly is meant by a building block in GP than what has traditionally been considered.
Genetic Programming: Evolving Simulated HumanGenerated Programs with Loops and Control Structures
"... ..."