Genetic Programming: Theory and Practice

Soule, Terence; Riolo, Rick L.; Worzel, Bill

doi:10.1007/0-387-23254-0_1

Cited by 33 publications

(19 citation statements)

References 7 publications

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“…GP is attractive to statistical model building because it allows to generate multiple empirical models and optimizing them from small data sets. 24 Starting from an initial set of models, GP allows a population of statistical models to evolve, through crossover and mutation operators, toward a global optimal solution. In this context, it means to find a model that optimally explains the growth of the vascular tissue from factors relevant to vascular mechanotransduction.…”

Section: Genetic Programmingmentioning

confidence: 99%

How to Optimize Maturation in a Bioreactor for Vascular Tissue Engineering: Focus on a Decision Algorithm for Experimental Planning

Couet

2010

Ann Biomed Eng

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Bioreactors may become essential tools for developing tissue-engineered organs from cells, with or without scaffolds. Cells seeded in these devices are expected to grow and form a tissue. Up to now, one of the main challenges to successfully develop functional organs is the structural organization of the cells into the scaffold during maturation in the bioreactor. The maturation step is affected by a set of highly interlinked dynamical variables (flow, stress, pH, temperature, and growth factors) in such a way that fixing the optimal environmental conditions becomes very complex. This work focuses on how the experimental parameters in the bioreactor can be optimized through numerical modeling to maximize tissue growth. Genetic programming (GP) and Markov decision processes (MDPs) were used in synergy to generate and take full advantage of a model of the vascular construct growth. The approach consists in formulating a model through GP to explain the growth of the construct and using MDPs to come up with a strategy to yield the best results in the experimental runs. Construct growth was improved, and the regeneration process was better understood in numerical simulations that relied on this control system. Therefore, an advanced numerical controller of this type could become an effective and inexpensive tool for planning experimental work in tissue engineering.

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Section: Genetic Programmingmentioning

confidence: 99%

How to Optimize Maturation in a Bioreactor for Vascular Tissue Engineering: Focus on a Decision Algorithm for Experimental Planning

Couet

2010

Ann Biomed Eng

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“…This is the result of theoreticians rarely being application people (and vice versa), and also of the lack of a forum where theoreticians and practitioners can meet and discuss (with the notable exception of the "Genetic Programming Theory and Practice" workshop series (Riolo and Worzel, 2003;O'Reilly et al, 2004;Yu et al, 2005;Riolo et al, 2007) the focus of which, however, is limited to genetic programming).…”

Section: Theory and Practice Don't Talk To Each Othermentioning

confidence: 99%

Editorial Introduction to the Special Issue

Hamzah

2007

Evolutionary Computation

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“…The details of GA are beyond the scope of this paper, but interested readers can refer to [25][26][27]. Most of evolutionary techniques have the following procedure: 1.…”

Section: Comparisons Between Ga and Psomentioning

confidence: 99%

Hardware Software Partitioning using Particle Swarm Optimization Technique

Abdelhalim

Salama

Habib

2006

2006 6th International Workshop on System on Chip for Real Time Applications

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In this paper we investigate the application of the Particle Swarm Optimization (PSO) technique for solving the Hardware/Software partitioning problem. The PSO is attractive for the Hardware/Software partitioning problem as it offers reasonable coverage of the design space together with O(n) main loop's execution time, where n is the number of proposed solutions that will evolve to provide the final solution. We carried out several tests on a hypothetical, relatively-large Hardware/Software partitioning problem using the PSO algorithm as well as the Genetic Algorithm (GA), which is another evolutionary technique. We found that PSO outperforms GA in the cost function and the execution time. For the case of unconstrained design problem, we tested several hybrid combinations of PSO and GA algorithms; including PSO then GA, GA then PSO, GA followed by GA, and finally PSO followed by PSO. We found that a PSO followed by GA algorithm gives small or no improvement at all, while a GA then PSO algorithm gives the same results as the PSO alone. The PSO algorithm followed by another PSO round gave the best result as it allows another round of domain exploration. The second PSO round assign new randomized velocities to the particles, while keeping best particle positions obtained in the first round. We propose to name this successive PSO algorithm as the Re-excited PSO algorithm.

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Genetic Programming: Theory and Practice

Cited by 33 publications

References 7 publications

How to Optimize Maturation in a Bioreactor for Vascular Tissue Engineering: Focus on a Decision Algorithm for Experimental Planning

How to Optimize Maturation in a Bioreactor for Vascular Tissue Engineering: Focus on a Decision Algorithm for Experimental Planning

Editorial Introduction to the Special Issue

Hardware Software Partitioning using Particle Swarm Optimization Technique

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