Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
High-level optimization via automated statistical modeling
PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Learning evaluation functions for global optimization and Boolean satisfiability
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Branch and bound algorithm selection by performance prediction
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Foundations of genetic programming
Foundations of genetic programming
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Genetic Programming and Evolvable Machines
Proceedings of the Seventeenth International Conference on Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Statistical Models for Automatic Performance Tuning
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Learning to Predict Performance from Formula Modeling and Training Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Practical Implications of New Results in Conservation of Optimizer Performance
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Fitness Distance Correlation And Problem Difficulty For Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Proceedings of the European Conference on Genetic Programming
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
On the Expected Runtime and the Success Probability of Evolutionary Algorithms
WG '00 Proceedings of the 26th International Workshop on Graph-Theoretic Concepts in Computer Science
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
Combinatorial Optimization through Statistical Instance-Based Learning
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
On classes of functions for which No Free Lunch results hold
Information Processing Letters
General schema theory for genetic programming with subtree-swapping crossover: Part II
Evolutionary Computation
Genetic Programming and Evolvable Machines
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
A framework for adaptive algorithm selection in STAPL
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
How randomized search heuristics find maximum cliques in planar graphs
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Genetic and Evolutionary Computation Conference
Evolutionary algorithms and matroid optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A markov chain framework for the simple genetic algorithm
Evolutionary Computation
Schemata evolution and building blocks
Evolutionary Computation
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Computing single source shortest paths using single-objective fitness
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Free lunches for function and program induction
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
There Is a Free Lunch for Hyper-Heuristics, Genetic Programming and Computer Scientists
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Empirical hardness models: Methodology and a case study on combinatorial auctions
Journal of the ACM (JACM)
Free lunches for neural network search
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
A portfolio approach to algorithm select
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Real royal road functions-where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
Difficulty of unimodal and multimodal landscapes in genetic programming
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Fitness distance correlation in structural mutation genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Hierarchical hardness models for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
SATzilla-07: the design and analysis of an algorithm portfolio for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
A survey of problem difficulty in genetic programming
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Performance prediction and automated tuning of randomized and parametric algorithms
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Negative slope coefficient: a measure to characterize genetic programming fitness landscapes
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Using CBR to select solution strategies in constraint programming
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Models of performance of time series forecasters
Neurocomputing
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Evolutionary computation techniques have seen a considerable popularity as problem solving and optimisation tools in recent years. Theoreticians have developed a variety of both exact and approximate models for evolutionary program induction algorithms. However, these models are often criticised for being only applicable to simplistic problems or algorithms with unrealistic parameters. In this paper, we start rectifying this situation in relation to what matters the most to practitioners and users of program induction systems: performance. That is, we introduce a simple and practical model for the performance of program-induction algorithms. To test our approach, we consider two important classes of problems - symbolic regression and Boolean function induction - and we model different versions of genetic programming, gene expression programming and stochastic iterated hill climbing in program space. We illustrate the generality of our technique by also accurately modelling the performance of a training algorithm for artificial neural networks and two heuristics for the off-line bin packing problem. We show that our models, besides performing accurate predictions, can help in the analysis and comparison of different algorithms and/or algorithms with different parameters setting. We illustrate this via the automatic construction of a taxonomy for the stochastic program-induction algorithms considered in this study. The taxonomy reveals important features of these algorithms from the performance point of view, which are not detected by ordinary experimentation.