A population-based evolutionary algorithm for sampling minima in the protein energy surface

2012 ◽  
Author(s):  
Sameh Saleh ◽  
Brian Olson ◽  
Amarda Shehu
Keyword(s):  
Evolutionary Algorithm ◽  
Energy Surface ◽  
Population Based ◽  
Protein Energy ◽  
Protein Energy Surface

scholarly journals Energy landscape in protein folding and unfolding

2016 ◽  
Vol 113 (12) ◽  
pp. 3159-3163 ◽  
Author(s):  
Francesco Mallamace ◽  
Carmelo Corsaro ◽  
Domenico Mallamace ◽  
Sebastiano Vasi ◽  
Cirino Vasi ◽  
...  
Keyword(s):  
Protein Folding ◽  
Hydrogen Bonding ◽  
Thermal Denaturation ◽  
Energy Surface ◽  
Energy Landscape ◽  
Denatured State ◽  
H Nmr ◽  
Protein Energy ◽  
Protein Energy Surface

We use 1H NMR to probe the energy landscape in the protein folding and unfolding process. Using the scheme ⇄ reversible unfolded (intermediate) → irreversible unfolded (denatured) state, we study the thermal denaturation of hydrated lysozyme that occurs when the temperature is increased. Using thermal cycles in the range 295<T<365 K and following different trajectories along the protein energy surface, we observe that the hydrophilic (the amide NH) and hydrophobic (methyl CH3 and methine CH) peptide groups evolve and exhibit different behaviors. We also discuss the role of water and hydrogen bonding in the protein configurational stability.

scholarly journals Parameterized Analysis of Multiobjective Evolutionary Algorithms and the Weighted Vertex Cover Problem

2019 ◽  
Vol 27 (4) ◽  
pp. 559-575
Author(s):  
Mojgan Pourhassan ◽  
Feng Shi ◽  
Frank Neumann
Keyword(s):  
Evolutionary Algorithm ◽  
Complexity Analysis ◽  
Minimum Weight ◽  
Vertex Cover ◽  
Optimal Solution ◽  
Population Based ◽  
Mutation Operator ◽  
Vertex Cover Problem ◽  
Weighted Vertex ◽  
Cover Problem

Evolutionary multiobjective optimization for the classical vertex cover problem has been analysed in Kratsch and Neumann ( 2013 ) in the context of parameterized complexity analysis. This article extends the analysis to the weighted vertex cover problem in which integer weights are assigned to the vertices and the goal is to find a vertex cover of minimum weight. Using an alternative mutation operator introduced in Kratsch and Neumann ( 2013 ), we provide a fixed parameter evolutionary algorithm with respect to [Formula: see text], the cost of an optimal solution for the problem. Moreover, we present a multiobjective evolutionary algorithm with standard mutation operator that keeps the population size in a polynomial order by means of a proper diversity mechanism, and therefore, manages to find a 2-approximation in expected polynomial time. We also introduce a population-based evolutionary algorithm which finds a [Formula: see text]-approximation in expected time [Formula: see text].

Imperialist Competitive Algorithms with Perturbed Moves for Global Optimization

2013 ◽  
Vol 284-287 ◽  
pp. 3135-3139 ◽  
Author(s):  
Jun Lin Lin ◽  
Chun Wei Cho ◽  
Hung Chjh Chuan
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithm ◽  
Imperialist Competitive Algorithm ◽  
Population Based ◽  
Experimental Results ◽  
Local Optimum ◽  
Competitive Algorithm ◽  
Competitive Algorithms

Imperialist Competitive Algorithm (ICA) is a new population-based evolutionary algorithm. Previous works have shown that ICA converges quickly but often to a local optimum. To overcome this problem, this work proposed two modifications to ICA: perturbed assimilation move and boundary bouncing. The proposed modifications were applied to ICA and tested using six well-known benchmark functions with 30 dimensions. The experimental results indicate that these two modifications significantly improve the performance of ICA on all six benchmark functions.

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