Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model

The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.     

Genetic algorithm with local optimization

A hybrid of genetic algorithm and local optimization was tested on a massively multimodal spin-lattice problem involving a huge configuration space. The results are good, and global optima will probably be achieved in a sizeable proportion of cases, especially if a selection scheme is applied that maintains genetic diversity by introducing a spatial separation between the members of the population. If we use single-point cross-over, the performance of the algorithm depends strongly on the order of the units corresponding to individual spins in the bit strings that the genetic part of the algorithm processes. Due to some interplay between the genetic algorithm and local optimization, the best performance is achieved with a peculiar ordering, while the results with the most obvious ordering are much worse. I introduce an ordering-invariant crossover operation that gives excellent performance: it almost always yields states of the lowest energy. I expect this or some similar crossover operation to work well in the hybrid scheme for many other problems as well.     

The hybrid bacterial foraging algorithm based on many-objective optimizer

A new multi-objective optimized bacterial foraging algorithm - Hybrid Multi-Objective Optimized Bacterial Foraging Algorithm (HMOBFA) is presented in this article. The proposed algorithm combines the crossover-archives strategy and the life-cycle optimization strategy, look for the best method through research area. The crossover-archive strategy with an external archive and internal archive is assigned to different selection principles to focus on diversity and convergence separately. Additionally, according to the local landscape to satisfy population diversity and variability as well as avoiding redundant local searches, individuals can switch their states periodically throughout the colony lifecycle with the life-cycle optimization strategy. all of which may perform significantly well. The performance of the algorithm was examined with several standard criterion functions and compared with other classical multi-objective majorization methods. The examiner results show that the HMOBFA algorithm can achieve a significant enhancement in performance compare with other method and handles many-objective issues with solid complexity, convergence as well as diversity. The HMOBFA algorithm has been proven to be an excellent alternative to past methods for solving the improvement of many-objective problems.     

Multipoint genetic mapping with uniparental disomy data

Uniparental disomy (UPD) refers to the presence of two copies of a chromosome from one parent and none from the other parent. In genetic studies of UPDs, many genetic markers are usually used to identify the stage of nondisjunction that leads to UPD and to uncover the associated unusual patterns of recombinations. However, genetic information in such data has not been fully utilized because of the limitations of the existing statistical methods for UPD data. In the present article, we develop a multilocus statistical approach that has the advantages of being able to simultaneously consider all genetic markers for all individuals in the same analysis and to allow general models for the crossover process to incorporate crossover interference. In particular, for a general crossover-process model that assumes only that there exists in each interval at most one crossover, we describe how to use the expectation-maximization algorithm to examine the probability distribution of the recombination events underlying meioses leading to UPD. We can also use this flexible approach to create genetic maps based on UPD data and to inspect recombination differences between meioses exhibiting UPD and normal meioses. The proposed method has been implemented in a computer program, and we illustrate the proposed approach through its application to a set of UPD15 data.     

Mutation-tolerant protein identification by mass spectrometry.

Database search in tandem mass spectrometry is a powerful tool for protein identification. High-throughput spectral acquisition raises the problem of dealing with genetic variation and peptide modifications within a population of related proteins. A method that cross-correlates and clusters related spectra in large collections of uncharacterized spectra (i.e., from normal and diseased individuals) would be very valuable in functional proteomics. This problem is far from being simple since very similar peptides may have very different spectra. We introduce a new notion of spectral similarity that allows one to identify related spectra even if the corresponding peptides have multiple modifications/mutations. Based on this notion, we developed a new algorithm for mutation-tolerant database search as well as a method for cross-correlating related uncharacterized spectra.     

Co-evolution of learning complexity and social foraging strategies

Variation in learning abilities within populations suggests that complex learning may not necessarily be more adaptive than simple learning. Yet, the high cost of complex learning cannot fully explain this variation without some understanding of why complex learning is too costly for some individuals but not for others. Here we propose that different social foraging strategies can favor different learning strategies (that learn the environment with high or low resolution), thereby maintaining variable learning abilities within populations. Using a genetic algorithm in an agent-based evolutionary simulation of a social foraging game (the producer-scrounger game) we demonstrate how an association evolves between a strategy based on independent search for food (playing a producer) and a complex (high resolution) learning rule, while a strategy that combines independent search and following others (playing a scrounger) evolves an association with a simple (low resolution) learning rule. The reason for these associations is that for complex learning to have an advantage, a large number of learning steps, normally not achieved by scroungers, are necessary. These results offer a general explanation for persistent variation in cognitive abilities that is based on co-evolution of learning rules and social foraging strategies.     

The Evolution of Quantitatively Responsive Cooperative Trade

The iterated Prisoner's Dilemma reflects the essence of repeated cooperative interactions with selfish incentives. However, the classical form of this game assumes that individuals either cooperate or defect, whereas in practice different degrees of cooperation are usually possible. To overcome this limitation, we present a model of alternating cooperative trade in which individuals controlled the costs they incurred in benefiting their partners. Since the range of possible strategies is enormous, competitively successful solutions were identified using a genetic algorithm, a powerful search technique in which good performers are iteratively selected and recombined from an initial "strategy soup". Beginning with a population of asocial individuals, altruistic behaviour readily emerged. Like the pre-defined strategy of "Raise-the-Stakes", the emerging strategies evolved protection from cheats by investing relatively little in strangers and subsequently responding quantitatively to a partner's altruism. Unlike "Raise-the-Stakes", they began trading relations at intermediate levels and, when the benefit-to-cost ratio of cooperation was relatively low, mean investment was considerably below the maximum level. Our approach is novel in allowing us to predict not just whether cooperation will occur, but how cooperative individuals will be, in relation to factors such as the number of rounds and the cost effectiveness of cooperative trade.     

Fine-Scale Heterogeneity in Crossover Rate in the garnet-scalloped Region of the Drosophila melanogaster X Chromosome

Homologous recombination affects myriad aspects of genome evolution, from standing levels of nucleotide diversity to the efficacy of natural selection. Rates of crossing over show marked variability at all scales surveyed, including species-, population-, and individual-level differences. Even within genomes, crossovers are nonrandomly distributed in a wide diversity of taxa. Although intra- and intergenomic heterogeneities in crossover distribution have been documented in Drosophila, the scale and degree of crossover rate heterogeneity remain unclear. In addition, the genetic features mediating this heterogeneity are unknown. Here we quantify fine-scale heterogeneity in crossover distribution in a 2.1-Mb region of the Drosophila melanogaster X chromosome by localizing crossover breakpoints in 2500 individuals, each containing a single crossover in this specific X chromosome region. We show 90-fold variation in rates of crossing over at a 5-kb scale, place this variation in the context of several aspects of genome evolution, and identify several genetic features associated with crossover rates. Our results shed new light on the scale and magnitude of crossover rate heterogeneity in D. melanogaster and highlight potential features mediating this heterogeneity.     

A genetic algorithm for maximum-likelihood phylogeny inference using nucleotide sequence data

Phylogeny reconstruction is a difficult computational problem, because the number of possible solutions increases with the number of included taxa. For example, for only 14 taxa, there are more than seven trillion possible unrooted phylogenetic trees. For this reason, phylogenetic inference methods commonly use clustering algorithms (e.g., the neighbor-joining method) or heuristic search strategies to minimize the amount of time spent evaluating nonoptimal trees. Even heuristic searches can be painfully slow, especially when computationally intensive optimality criteria such as maximum likelihood are used. I describe here a different approach to heuristic searching (using a genetic algorithm) that can tremendously reduce the time required for maximum-likelihood phylogenetic inference, especially for data sets involving large numbers of taxa. Genetic algorithms are simulations of natural selection in which individuals are encoded solutions to the problem of interest. Here, labeled phylogenetic trees are the individuals, and differential reproduction is effected by allowing the number of offspring produced by each individual to be proportional to that individual's rank likelihood score. Natural selection increases the average likelihood in the evolving population of phylogenetic trees, and the genetic algorithm is allowed to proceed until the likelihood of the best individual ceases to improve over time. An example is presented involving rbcL sequence data for 55 taxa of green plants. The genetic algorithm described here required only 6% of the computational effort required by a conventional heuristic search using tree bisection/reconnection (TBR) branch swapping to obtain the same maximum-likelihood topology.      

基于遗传算法预测2D三向的蛋白质结构

本文基于范德华力势能预测2D三向的蛋白质结构。首先,将蛋白质结构预测这一生物问题转化为数学问题,并建立基于范德华力势能函数的数学模型。其次,使用遗传算法对数学模型进行求解,为了提高蛋白质结构预测效率,我们在标准遗传算法的基础上引入了调整算子这一概念,改进了遗传算法。最后,进行数值模拟实验。实验的结果表明范德华力势能函数模型是可行的,同时,和规范遗传算法相比,改进后的遗传算法能够较大幅度提高算法的搜索效率,并且遗传算法在蛋白质结构预测问题上有巨大潜力。     

Exact methods for the robotic cell problem

This paper investigates an exact method for the Robotic Cell Problem. We present a branch-and-bound algorithm which is the first exact procedure specifically designed with regard to this complex flow shop scheduling variant. Also, we propose a new mathematical programming model as well as new lower bounds. Furthermore, we describe an effective genetic algorithm that includes, as a mutation operator, a local search procedure. We report the results of a computational study that provides evidence that medium-sized instances, with up to 176 operations, can be optimally solved. Also, we found that the new proposed lower bounds outperform lower bounds from the literature. Finally, we show, that the genetic algorithm delivers good solutions while requiring short CPU times.     

Enhancing distributed EAs by a proactive strategy

In this work we propose a new distributed evolutionary algorithm that uses a proactive strategy to adapt its migration policy and the mutation rate. The proactive decision is carried out locally in each subpopulation based on the entropy of that subpopulation. In that way, each subpopulation can change their own incoming flow of individuals by asking their neighbors for more frequent or less frequent migrations in order to maintain the genetic diversity at a desired level. Moreover, this proactive strategy is reinforced by adapting the mutation rate while the algorithm is searching for the problem solution. All these strategies avoid the subpopulations to get trapped into local minima. We conduct computational experiments on large instances of the NK landscape problem which have shown that our proactive approach outperforms traditional dEAs, particularly for not highly rugged landscapes, in which it does not only reaches the most accurate solutions, but it does the fastest.     

Memetic algorithms for the unconstrained binary quadratic programming problem

This paper presents a memetic algorithm, a highly effective evolutionary algorithm incorporating local search for solving the unconstrained binary quadratic programming problem (BQP). To justify the approach, a fitness landscape analysis is conducted experimentally for several instances of the BQP. The results of the analysis show that recombination-based variation operators are well suited for the evolutionary algorithms with local search. Therefore, the proposed approach includes--besides a highly effective randomized k-opt local search--a new variation operator that has been tailored specially for the application in the hybrid evolutionary framework. The operator is called innovative variation and is fundamentally different from traditional crossover operators, since new genetic material is included in the offspring which is not contained in one of the parents. The evolutionary heuristic is tested on 35 publicly available BQP instances, and it is shown experimentally that the algorithm is capable of finding best-known solutions to large BQPs in a short time and with a high frequency. In comparison to other approaches for the BQP, the approach appears to be much more effective, particularly for large instances of 1000 or 2500 binary variables.     

研究报告: 基于遗传算法的计算机辅助策略优化孕酮适配体

目的 本研究致力于优化孕酮（progesterone，P4）适配体的亲和力和选择性。方法 基于遗传算法（genetic algorithm，GA）的计算机辅助优化策略（in silico maturation，ISM），进行了4轮GA操作（含交叉变异、单点突变和双点突变操作），构建了初始文库和G1、G2、G3代ssDNA作为新的候选适配体库，采用分子对接对候选适配体进行筛选和分析，并使用迭代策略不断优化适配体。此外，还提出了一种较为准确预测ssDNA三级结构的方法，首先使用Mfold预测二级结构，继而使用RNAComposer建立与ssDNA相对应的RNA三级结构，输出的PDB文件使用Discovery Studio将RNA修改为DNA，最后使用Molecular Operating Environment对结构进行能量最小化处理。结果 到G2代，在局部搜索空间对P4S-0进行优化，筛选出P4G1-14、P4G2-20、P4G1-6、P4G1-7和P4G2-14这5条适配体作为P4的最佳候选适配体。采用AuNPs比色法初步验证优化后适配体的亲和力，继而构建了基于适配体结构开关的荧光法测定适配体的解离常数（equilibrium dissociation constant，K_D），并以此方法对适配体的选择性（对双酚A、雌二醇、睾酮和皮质醇）进行了评估。结论 通过ISM优化后的适配体，对P4的亲和力较原适配体有了较大提升，仍保留着识别结构类似分子的选择性。     

Genetic rescue guidelines with examples from Mexican wolves and Florida panthers

In populations or species with low fitness (high genetic load), a new management strategy called genetic rescue has been advocated to help avoid extinction. In this strategy, unrelated individuals from another population are introduced into the population with low fitness in an effort to reduce genetic load. Here we present ten guidelines that can be used to evaluate when genetic rescue is a good management option, the appropriate procedures for genetic rescue planning and management, and the potential negative genetic consequences of genetic rescue. These guidelines are then used to evaluate the genetic rescue aspects of the recovery programs for the Mexican wolf and the Florida panther.     

Dispersal mood revealed by shifts from routine to direct flights in the meadow brown butterfly Maniola jurtina

A comprehensive mechanistic approach to dispersal requires the translation of the whole mobility register of the target organism into movement rules that could subsequently be used to model its displacements. According to the optimality paradigm, this procedure implies a cost–benefit analysis of mobility patterns taking into account not only movements, but also their external context and the internal state of the moving individuals. Using this framework, we detected a ‘dispersal mood’ in some individuals of the meadow brown butterfly Maniola jurtina. These adopted a direct flight strategy, which was topologically different from the previously documented foray search strategy. Those individuals that used the direct flight strategy moved straighter as soon as they left the habitat and avoided heading back to their patch of origin, which is the best inter‐patch search strategy when dispersal risks and costs are high. The direct flight strategy was conditional to sex: females used it twice as much as males. We suggest that this sex bias was due to female investment in offspring, which is maximized by male avoidance and spatial bet hedging. Inter‐patch dispersal of gravid females is crucial for the persistence of M. jurtina populations in spatially and temporally unpredictable environments.     

How the value of the environment controls persistence in visual search

Classic foraging theory predicts that humans and animals aim to gain maximum reward per unit time. However, in standard instrumental conditioning tasks individuals adopt an apparently suboptimal strategy: they respond slowly when the expected value is low. This reward-related bias is often explained as reduced motivation in response to low rewards. Here we present evidence this behavior is associated with a complementary increased motivation to search the environment for alternatives. We trained monkeys to search for reward-related visual targets in environments with different values. We found that the reward-related bias scaled with environment value, was consistent with persistent searching after the target was already found, and was associated with increased exploratory gaze to objects in the environment. A novel computational model of foraging suggests that this search strategy could be adaptive in naturalistic settings where both environments and the objects within them provide partial information about hidden, uncertain rewards.     

基于混合遗传算法的偶极子源定位的仿真计算

首先提出一种新的混合遗传算法。在基于实数编码的基础上,通过嵌入一个最速下降算子,结合遗传算法和最速下降法两者的优点,并引入模拟退火的思想,即可改善原算法的局部搜索能力,又能进一步提高优化效率。为了验证算法的可行性,通过对脑电偶极子源定位的仿真计算,证明所提出的新算法与其它优化算法相比,在达到最优解的效率上有了明显的提高。     

Coevolving solutions to the shortest common superstring problem

The shortest common superstring (SCS) problem, known to be NP-Complete, seeks the shortest string that contains all strings from a given set. In this paper we compare four approaches for finding solutions to the SCS problem: a standard genetic algorithm, a novel cooperative-coevolutionary algorithm, a benchmark greedy algorithm, and a parallel coevolutionary-greedy approach. We show the coevolutionary approach produces the best results, and discuss directions for future research.