Multi-objectivization, fitness landscape transformation and search performance: A case of study on the hp model for protein structure prediction

Abstract

a b s t r a c tMulti-objectivization represents a current and promising research direction which has led to the development of more competitive search mechanisms. This concept involves the restatement of a single-objective problem in an alternative multi-objective form, which can facilitate the process of finding a solution to the original problem. Recently, this transformation was applied with success to the HP model, a simplified yet challenging representation of the protein structure prediction problem. The use of alternative multi-objective formulations, based on the decomposition of the original objective function of the problem, has significantly increased the performance of search algorithms. The present study goes further on this topic. With the primary aim of understanding and quantifying the potential effects of multi-objectivization, a detailed analysis is first conducted to evaluate the extent to which this problem transformation impacts on an important characteristic of the fitness landscape, neutrality. To the authors' knowledge, the effects of multi-objectivization have not been previously investigated by explicitly sampling and evaluating the neutrality of the fitness landscape. Although focused on the HP model, most of the findings of such an analysis can be extrapolated to other problem domains, contributing thus to the general understanding of multi-objectivization. Finally, this study presents a comparative analysis where the advantages of multi-objectivization are evaluated in terms of the performance of a basic evolutionary algorithm. Both the two-and three-dimensional variants of the HP model (based on the square and cubic lattices, respectively) are considered.

A Novel Framework for Ab Initio Coarse Protein Structure Prediction

“…To evaluate the efficiency of the proposed initialization approach, both proposed and random initialization programs have been executed to generate 100 conformations for the benchmark sequences [ 18 ] presented in Table 1 . Both of the initialization programs were implemented locally on the same system.…”

“…The proposed framework has been evaluated with five state-of-the-art approaches, namely, MMA [ 37 ], ACO [ 34 ], IA [ 35 ], GGA [ 18 ], and CMA [ 47 ]. The proposed framework is implemented with an initial population of 100 conformations.…”

“…The test HP sequences ( Table 1 ) were taken from the study of Garza-Fabre et al [ 18 ]. Initially, conformations are built using random generation technique.…”

“…In 1985, Ken Dill [ 17 ] proposed a new computational perspective to lessen the modelling complexity and accelerate structure prediction to fill the sequence-structure gap, namely, the Hydrophobic-Polar (HP) model. Contrary to molecular mechanics and other modelling methods, the HP model considers hydrophobic interaction as the primary force involved in the folding process and yet was able to depict the natural folding patterns [ 18 ]. This is the most widely accepted approach to solve the PSP problem [ 19 – 21 ].…”

“…In order to address NP-hardness , numerous researchers have worked with various population-based search algorithms such as Monte Carlo [ 23 – 25 ], genetic algorithm [ 19 , 26 – 30 ], evolutionary programming [ 31 ], ant colony optimization [ 32 – 34 ], immune algorithm [ 35 ], constraint based chain growth algorithm [ 36 ], and hybrid of local search and evolutionary algorithms commonly called memetic algorithm [ 37 – 41 ]. The details on these algorithms and their performance are available in various review papers [ 18 , 42 ].…”