Computers and Operations Research
Membrane Computing: An Introduction
Membrane Computing: An Introduction
Journal of Global Optimization
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Communications of the ACM - Security in the Browser
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Computers and Operations Research
On p systems as a modelling tool for biological systems
WMC'05 Proceedings of the 6th international conference on Membrane Computing
P systems, a new computational modelling tool for systems biology
Transactions on Computational Systems Biology VI
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
Executable Biology, also called Algorithmic Systems Biology, uses rigorous concepts from computer science and mathematics to build computational models of biological entities. P systems are emerging as one of the key modelling frameworks within Executable Biology. In this paper, we address the continuous backward problem: given a P system model structure and a target phenotype (i.e. an intended biological behaviour), one is tasked with finding the (near) optimal parameters for the model that would make the P system model produce the target behaviour as closely as possible. We test several real-valued parameter optimisation algorithms on this problem. More specifically, using four different test cases of increasing complexity, we perform experiments with four evolutionary algorithms, and one variable neighbourhood search method combining three other evolutionary algorithms. The results show that, when there are few parameters to optimise, a genetic and two differential evolution based algorithms are robust optimisers attaining the best results. However, when the number of parameters increases, the variable neighbourhood search approach performs better.