Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
High-level optimization via automated statistical modeling
PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
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
ACM Computing Surveys (CSUR)
Foundations of genetic programming
Foundations of genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
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
Fitness Distance Correlation And Problem Difficulty For Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Proceedings of the European Conference on Genetic Programming
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
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Combinatorial Optimization through Statistical Instance-Based Learning
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
General schema theory for genetic programming with subtree-swapping crossover: Part II
Evolutionary Computation
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
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
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
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
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Empirical hardness models: Methodology and a case study on combinatorial auctions
Journal of the ACM (JACM)
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
Difficulty of unimodal and multimodal landscapes in genetic programming
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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
Practical model of genetic programming's performance on rational symbolic regression problems
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Human-competitive results produced by genetic programming
Genetic Programming and Evolvable Machines
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
Defining locality as a problem difficulty measure in genetic programming
Genetic Programming and Evolvable Machines
A bayesian approach to tackling hard computational problems
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
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
On the locality of grammatical evolution
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
IEEE Transactions on Evolutionary Computation
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Modeling the behavior of algorithms is the realm of evolutionary algorithm theory. From a practitioner's point of view, theory must provide some guidelines regarding which algorithm/parameters to use in order to solve a particular problem. Unfortunately, most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. However, in recent work Graff and Poli, 2008, 2010, where we developed a method to practically estimate the performance of evolutionary program-induction algorithms EPAs, we started addressing this issue. The method was quite general; however, it suffered from some limitations: it required the identification of a set of reference problems, it required hand picking a distance measure in each particular domain, and the resulting models were opaque, typically being linear combinations of 100 features or more. In this paper, we propose a significant improvement of this technique that overcomes the three limitations of our previous method. We achieve this through the use of a novel set of features for assessing problem difficulty for EPAs which are very general, essentially based on the notion of finite difference. To show the capabilities or our technique and to compare it with our previous performance models, we create models for the same two important classes of problems-symbolic regression on rational functions and Boolean function induction-used in our previous work. We model a variety of EPAs. The comparison showed that for the majority of the algorithms and problem classes, the new method produced much simpler and more accurate models than before. To further illustrate the practicality of the technique and its generality beyond EPAs, we have also used it to predict the performance of both autoregressive models and EPAs on the problem of wind speed forecasting, obtaining simpler and more accurate models that outperform in all cases our previous performance models.