Genetic algorithms are founded upon the principle of evolution, ie, survival of the fittest.
Hence evolution programming techniques, based on genetic algorithms, are applicable to …

Metaheuristics are the most exciting development in approximate optimization techniques of
the last two decades. They have had widespread successes in attacking a variety of difficult …

Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimizing real-
valued multi-modal functions. Function parameters are encoded as floating-point variables …

Motivation Kinetic models contain unknown parameters that are estimated by optimizing the
fit to experimental data. This task can be computationally challenging due to the presence of …

The dynamic optimization (open loop optimal control) of non-linear bioprocesses is
considered in this contribution. These processes can be described by sets of non-linear …

Background Optimization is the key to solving many problems in computational biology.
Global optimization methods, which provide a robust methodology, and metaheuristics in …

Ordinary differential equation models have become a standard tool for the mechanistic
description of biochemical processes. If parameters are inferred from experimental data …

An enhanced scatter search method for the global dynamic optimization of nonlinear
processes using the control vector parametrization (CVP) approach is presented. Sharing …

Despite significant efforts and remarkable progress, the inference of signaling networks from
experimental data remains very challenging. The problem is particularly difficult when the …

Background Mathematical models play a key role in systems biology: they summarize the
currently available knowledge in a way that allows to make experimentally verifiable …