Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Some guidelines for genetic algorithms with penalty functions
Proceedings of the third international conference on Genetic algorithms
Journal of Computational Physics
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic programming and emergent intelligence
Advances in genetic programming
Evolvable 3D modeling for model-based object recognition systems
Advances in genetic programming
Genetic micro programming of neural networks
Advances in genetic programming
On using syntactic constraints with genetic programming
Advances in genetic programming
Computationally Manageable Combinational Auctions
Management Science
A weight-coded genetic algorithm for the multiple container packing problem
Proceedings of the 1999 ACM symposium on Applied computing
The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System
IEEE Expert: Intelligent Systems and Their Applications
Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithms for the Multiple Container Packing Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Significance of Locality and Selection Pressure in the Grand Deluge Evolutionary Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Characterizing Locality in Decoder-Based EAs for the Multidimensional Knapsack Problem
AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
Grammatical bias for evolutionary learning
Grammatical bias for evolutionary learning
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Strongly typed genetic programming
Evolutionary Computation
Genetic programming using genotype-phenotype mapping from linear genomes into linear phenotypes
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Search bias, language bias and genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Learning recursive functions from noisy examples using generic genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Grammar-based Genetic Programming: a survey
Genetic Programming and Evolvable Machines
A grammatical evolution approach for software effort estimation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In this paper, we introduce genetic programming over context-free languages with linear constraints for combinatorial optimization, apply this method to several variants of the multidimensional knapsack problem, and discuss its performance relative to Michalewicz's genetic algorithm with penalty functions. With respect to Michalewicz's approach, we demonstrate that genetic programming over context-free languages with linear constraints improves convergence. A final result is that genetic programming over context-free languages with linear constraints is ideally suited to modeling complementarities between items in a knapsack problem: The more complementarities in the problem, the stronger the performance in comparison to its competitors.