On the use of multi-objective evolutionary algorithms for the induction of fuzzy classification rule systems

Extracting comprehensible and general classifiers from data in the form of rule systems is an important task in many problem domains. This study investigates the utility of a multi-objective evolutionary algorithm (MOEA) for this task. Multi-objective evolutionary algorithms are capable of finding several trade-off solutions between different objectives in a single run. In the context of the present study, the objectives to be optimised are the complexity of the rule systems, and their fit to the data. Complex rule systems are required to fit the data well. However, overly complex rule systems often generalise poorly on new data. In addition they tend to be incomprehensible. It is, therefore, important to obtain trade-off solutions that achieve the best possible fit to the data with the lowest possible complexity. The rule systems produced by the proposed multi-objective evolutionary algorithm are compared with those produced by several other existing approaches for a number of benchmark datasets. It is shown that the algorithm produces less complex classifiers that perform well on unseen data.     

Finding scalable configurations for AEDB broadcasting protocol using multi-objective evolutionary algorithms

Energy consumption is one of the main concerns in mobile ad hoc networks (or MANETs). The lifetime of its devices highly depends on the energy consumption as they rely on batteries. The adaptive enhanced distance based broadcasting algorithm, AEDB, is a message dissemination protocol for MANETs that uses cross-layer technology to highly reduce the energy consumption of devices in the process, while still providing competitive performance in terms of coverage and time. We use two different multi-objective evolutionary algorithms to optimize the protocol on three network densities, and we evaluate the scalability of the best found AEDB configurations on larger networks and different densities.     

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

Background  

Peptides binding to Major Histocompatibility Complex (MHC) class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides.     

Robustness analysis of EGFR signaling network with a multi-objective evolutionary algorithm

Robustness, the ability to maintain performance in the face of perturbations and uncertainty, is believed to be a necessary property of biological systems. In this paper, we address the issue of robustness in an important signal transduction network--epidermal growth factor receptor (EGFR) network. First, we analyze the robustness in the EGFR signaling network using all rate constants against the Gauss variation which was described as "the reference parameter set" in the previous study [Kholodenko, B.N., Demin, O.V., Moehren, G., Hoek, J.B., 1999. Quantification of short term signaling by the epidermal growth factor receptor. J. Biol. Chem. 274, 30169-30181]. The simulation results show that signal time, signal duration and signal amplitude of the EGRR signaling network are relatively not robust against the simultaneous variation of the reference parameter set. Second, robustness is quantified using some statistical quantities. Finally, a multi-objective evolutionary algorithm (MOEA) is presented to search reaction rate constants which optimize the robustness of network and compared with the NSGA-II, which is a representation of a class of modern multi-objective evolutionary algorithms. Our simulation results demonstrate that signal time, signal duration and signal amplitude of the four key components--the most downstream variable in each of the pathways: R-Sh-G-S, R-PLP, R-G-S and the phosphorylated receptor RP in EGRR signaling network for the optimized parameter sets have better robustness than those for the reference parameter set and the NSGA-II. These results can provide valuable insight into experimental designs and the dynamics of the signal-response relationship between the dimerized and activated EGFR and the activation of downstream proteins.     

On the best evolutionary rate for phylogenetic analysis

The effect of the evolutionary rate of a gene on the accuracy of phylogeny reconstruction was examined by computer stimulation. The evolutionary rate is measured by the tree length, that is, the expected total number of nucleotide substitutions per site on the phylogeny. DNA sequence data were simulated using both fixed trees with specified branch lengths and random trees with branch lengths generated from a model of cladogenesis. The parsimony and likelihood methods were used for phylogeny reconstruction, and the proportion of correctly recovered branch partitions by each method was estimated. Phylogenetic methods including parsimony appear quite tolerant of multiple substitutions at the same site. The optimum levels of sequence divergence were even higher than upper limits previously suggested for saturation of substitutions, indicating that the problem of saturation may have been exaggerated. Instead, the lack of information at low levels of divergence should be seriously considered in evaluation of a gene's phylogenetic utility, especially when the gene sequence is short. The performance of parsimony, relative to that of likelihood, does not necessarily decrease with the increase of the evolutionary rate.     

Inference of Euler angles for single-particle analysis by means of evolutionary algorithms

Single-particle analysis is one of the methods for structural studies of protein and macromolecules; it requires advanced image analysis of electron micrographics. Reconstructing three-dimensional (3D) structure from microscope images is not an easy analysis because of the low image resolution of images and lack of the directional information of images in 3D structure. To improve the resolution, different projections are aligned, classified, and averaged. Inferring the orientations of these images is so difficult that the task of reconstructing 3D structures depends upon the experience of researchers. But recently, a method to reconstruct 3D structures was automatically devised. In this paper, we propose a new method for determining Euler angles of projections by applying genetic algorithms. We empirically show that the proposed approach has improved the previous one in terms of computational time and acquired precision.     

Evolutionary algorithms for solving multi-objective travelling salesman problem

This paper studies the application of evolutionary algorithms for bi-objective travelling salesman problem. Two evolutionary algorithms, including estimation of distribution algorithm (EDA) and genetic algorithm (GA), are considered. The solution to this problem is a set of trade-off alternatives. The problem is solved by optimizing the order of the cities so as to simultaneously minimize the two objectives of travelling distance and travelling cost incurred by the travelling salesman. In this paper, binary-representation-based evolutionary algorithms are replaced with an integer-representation. Three existing EDAs are altered to use this integer-representation, namely restricted Boltzmann machine (RBM), univariate marginal distribution algorithm (UMDA), and population-based incremental learning (PBIL). Each city is associated with a representative integer, and the probability of any of this representative integer to be located in any position of the chromosome is constructed through the modeling approach of the EDAs. New sequences of cities are obtained by sampling from the probabilistic model. A refinement operator and a local search operator are proposed in this piece of work. The EDAs are subsequently hybridized with GA in order to complement the limitations of both algorithms. The effect that each of these operators has on the quality of the solutions are investigated. Empirical results show that the hybrid algorithms are capable of finding a set of good trade-off solutions.     

An experimental analysis of design choices of multi-objective ant colony optimization algorithms

There have been several proposals on how to apply the ant colony optimization (ACO) metaheuristic to multi-objective combinatorial optimization problems (MOCOPs). This paper proposes a new formulation of these multi-objective ant colony optimization (MOACO) algorithms. This formulation is based on adding specific algorithm components for tackling multiple objectives to the basic ACO metaheuristic. Examples of these components are how to represent multiple objectives using pheromone and heuristic information, how to select the best solutions for updating the pheromone information, and how to define and use weights to aggregate the different objectives. This formulation reveals more similarities than previously thought in the design choices made in existing MOACO algorithms. The main contribution of this paper is an experimental analysis of how particular design choices affect the quality and the shape of the Pareto front approximations generated by each MOACO algorithm. This study provides general guidelines to understand how MOACO algorithms work, and how to improve their design.     

Efficient algorithms for protein sequence design and the analysis of certain evolutionary fitness landscapes.

Protein sequence design is a natural inverse problem to protein structure prediction: given a target structure in three dimensions, we wish to design an amino acid sequence that is likely fold to it. A model of Sun, Brem, Chan, and Dill casts this problem as an optimization on a space of sequences of hydrophobic (H) and polar (P) monomers; the goal is to find a sequence that achieves a dense hydrophobic core with few solvent-exposed hydrophobic residues. Sun et al. developed a heuristic method to search the space of sequences, without a guarantee of optimality or near-optimality; Hart subsequently raised the computational tractability of constructing an optimal sequence in this model as an open question. Here we resolve this question by providing an efficient algorithm to construct optimal sequences; our algorithm has a polynomial running time, and performs very efficiently in practice. We illustrate the implementation of our method on structures drawn from the Protein Data Bank. We also consider extensions of the model to larger amino acid alphabets, as a way to overcome the limitations of the binary H/P alphabet. We show that for a natural class of arbitrarily large alphabets, it remains possible to design optimal sequences efficiently. Finally, we analyze some of the consequences of this sequence design model for the study of evolutionary fitness landscapes. A given target structure may have many sequences that are optimal in the model of Sun et al.; following a notion raised by the work of J. Maynard Smith, we can ask whether these optimal sequences are "connected" by successive point mutations. We provide a polynomial-time algorithm to decide this connectedness property, relative to a given target structure. We develop the algorithm by first solving an analogous problem expressed in terms of submodular functions, a fundamental object of study in combinatorial optimization.     

On the inappropriate use of the naturalistic fallacy in evolutionary psychology

The naturalistic fallacy is mentionedfrequently by evolutionary psychologists as anerroneous way of thinking about the ethicalimplications of evolved behaviors. However,evolutionary psychologists are themselvesconfused about the naturalistic fallacy and useit inappropriately to forestall legitimateethical discussion. We briefly review what thenaturalistic fallacy is and why it is misusedby evolutionary psychologists. Then we attemptto show how the ethical implications of evolvedbehaviors can be discussed constructivelywithout impeding evolutionary psychologicalresearch. A key is to show how ethicalbehaviors, in addition to unethical behaviors,can evolve by natural selection.     

First hitting time analysis of continuous evolutionary algorithms based on average gain

Runtime analysis of continuous evolutionary algorithms (EAs) is a hard topic in the theoretical research of evolutionary computation, relatively few results have been obtained compared to the discrete EAs. In this paper, we introduce the martingale and stopping time theory to establish a general average gain model to estimate the upper bound for the expected first hitting time. The proposed model is established on a non-negative stochastic process and does not need the assumption of Markov property, thus is more general. Afterwards, we demonstrate how the proposed model can be applied to runtime analysis of continuous EAs. In the end, as a case study, we analyze the runtime of (1, \(\lambda )\)ES with adaptive step-size on Sphere function using the proposed approach, and derive a closed-form expression of the time upper bound for 3-dimensional case. We also discuss the relationship between the step size and the offspring size \(\lambda \) to ensure convergence. The experimental results show that the proposed approach helps to derive tight upper bound for the expected first hitting time of continuous EAs.     

Using traveling salesman problem algorithms for evolutionary tree construction

MOTIVATION: The construction of evolutionary trees is one of the major problems in computational biology, mainly due to its complexity. RESULTS: We present a new tree construction method that constructs a tree with minimum score for a given set of sequences, where the score is the amount of evolution measured in PAM distances. To do this, the problem of tree construction is reduced to the Traveling Salesman Problem (TSP). The input for the TSP algorithm are the pairwise distances of the sequences and the output is a circular tour through the optimal, unknown tree plus the minimum score of the tree. The circular order and the score can be used to construct the topology of the optimal tree. Our method can be used for any scoring function that correlates to the amount of changes along the branches of an evolutionary tree, for instance it could also be used for parsimony scores, but it cannot be used for least squares fit of distances. A TSP solution reduces the space of all possible trees to 2n. Using this order, we can guarantee that we reconstruct a correct evolutionary tree if the absolute value of the error for each distance measurement is smaller than f2.gif" BORDER="0">, where f3.gif" BORDER="0">is the length of the shortest edge in the tree. For data sets with large errors, a dynamic programming approach is used to reconstruct the tree. Finally simulations and experiments with real data are shown.     

Comparison of genetic algorithms for experimental multi-objective optimization on the example of medium design for cyanobacteria

In this work, two different genetic algorithms were applied to improve culture media composition for the autotrophic cyanobacteria Synechococcus PCC 7942. Biomass yield and conversion of the asymmetric reduction of 2', 3', 4', 5', 6'-pentafluoroacetophenone were considered as simultaneous objectives, resulting in a multi-objective optimization problem. Even when similar performances of both algorithms were observed, it could be shown that a novel strength pareto approach was able to achieve remarkable results with a reduced number of experiments (160 instead of 320). Handling a high number of media components (13), their concentrations were adjusted, delivering high improvements in comparison to the standard BG 11 culture media. The quality of the Synechococcus biocatalyst could be increased up to fivefold compared to the initial state of the optimization.     

On the use of median‐joining networks in evolutionary biology

Median‐joining (MJ) was proposed as a method for phylogeographical analysis and is enjoying increasing popularity. Herein, we evaluate the efficacy of the approach as originally intended. We show that median‐joining networks (MJNs) are theoretically untenable for evolutionary inference, and that confusion has afflicted their use for over 15 years. The approach has two obvious shortcomings: its reliance on distance‐based phenetics (overall similarity instead of character transformations) and the lack of rooting (no direction or history). Given that evolution involves both change and time, and the absence of rooting removes time (ancestor–descendant relationships) from the equation, the approach cannot yield defensible evolutionary interpretations. We also examine the impact of MJ analyses on evolutionary biology via an analysis of citations and conclude that the spread of MJNs through the literature is difficult to explain, especially given the availability of character‐based analyses.     

On the use of size functions for shape analysis

According to a recent mathematical theory a shape can be represented by size functions, which convey information on both the topological and metric properties of the viewed shape. In this paper the relevance of the theory of size functions to visual perception is investigated. An algorithm for the computation of the size functions is presented, and many theoretical properties of the theory are demonstrated on real images. It is shown that the representation of shape in terms of size functions (1) can be tailored to suit the invariance of the problem at hand and (2) is stable against small qualitative and quantitative changes of the viewed shape. A distance between size functions is used as a measure of similarity between the representations of two different shapes. The results obtained indicate that size functions are likely to be very useful for object recognition. In particular, they seem to be well suited for the recognition of natural and articulated objects.     

Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times

Flexible Services and Manufacturing Journal - This paper proposes a single machine scheduling problem with learning-effect and release times by considering two objectives requiring minimization of...     

On null hypotheses in survival analysis

The conventional nonparametric tests in survival analysis, such as the log‐rank test, assess the null hypothesis that the hazards are equal at all times. However, hazards are hard to interpret causally, and other null hypotheses are more relevant in many scenarios with survival outcomes. To allow for a wider range of null hypotheses, we present a generic approach to define test statistics. This approach utilizes the fact that a wide range of common parameters in survival analysis can be expressed as solutions of differential equations. Thereby, we can test hypotheses based on survival parameters that solve differential equations driven by cumulative hazards, and it is easy to implement the tests on a computer. We present simulations, suggesting that our tests perform well for several hypotheses in a range of scenarios. As an illustration, we apply our tests to evaluate the effect of adjuvant chemotherapies in patients with colon cancer, using data from a randomized controlled trial.