F. Lobo

Publications
Influence

G. Harik, F. Lobo, D. Goldberg
Mathematics, Computer Science
IEEE Trans. Evol. Comput.
1 November 1999

Introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. Expand

A Survey of Optimization by Building and Using Probabilistic Models

M. Pelikan, D. Goldberg, F. Lobo
Mathematics, Computer Science
Comput. Optim. Appl.
2002

This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the exploration of the search space. Expand

A parameter-less genetic algorithm

G. Harik, F. Lobo
Mathematics, Computer Science
GECCO
13 July 1999

We propose a parameter-less genetic algorithm that relieves the user from having to set the parameters of the GA. Expand

Parameter Setting in Evolutionary Algorithms

F. Lobo, C. Lima, Z. Michalewicz
Computer Science
Studies in Computational Intelligence
19 April 2007

This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. Expand

Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA)

G. Harik, F. Lobo, K. Sastry
Mathematics, Computer Science
Scalable Optimization via Probabilistic Modeling
2006

This chapter explores the relationship between the linkage-learning problem and that of learning probability distributions over multi-variate spaces. Expand

Decision making in a hybrid genetic algorithm

F. Lobo, D. Goldberg
Computer Science
Proceedings of IEEE International Conference on…
13 April 1997

We present a model for hybridizing genetic algorithms (GAs) based on a concept that decision theorists call probability matching and we use it to combine an elitist selecto-recombinative GA with a simple hill climber (HC). Expand

Influence of selection and replacement strategies on linkage learning in BOA

C. Lima, M. Pelikan, D. Goldberg, F. Lobo, K. Sastry, Mark Hauschild
Computer Science
IEEE Congress on Evolutionary Computation
1 September 2007

The Bayesian optimization algorithm (BOA) uses Bayesian networks to learn linkages between the decision variables of an optimization problem. Expand

Multi-objective evolutionary algorithm for land-use management problem

K. Deb, Dilip Datta, C. M. Fonseca, F. Lobo, P. Condado, J. Seixas
Computer Science
2007

Land-use management problem/practice may be defined as the process of allocating different competitive land uses/activities, such as agriculture, forest, industries, recreational activities or conservation, to different units of a landscape to meet the desired objectives of land managers. Expand

The parameter-less genetic algorithm in practice

F. Lobo, D. Goldberg
Mathematics, Computer Science
Inf. Sci.
2 December 2004

This paper shows how the parameter-less genetic algorithm can be used in practice by applying it to a network expansion problem. Expand

Time Complexity of genetic algorithms on exponentially scaled problems

F. Lobo, D. Goldberg, M. Pelikan
Computer Science
GECCO
10 July 2000

This paper gives a theoretical and empirical analysis of the time complexity of genetic algorithms (GAs) on problems with exponentially scaled building blocks. Expand

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