Neutral genetic drift: an investigation using Cartesian Genetic Programming

Dispatching rules are most commonly used to solve scheduling problems under dynamic conditions. Since designing new dispatching rules is a time-consuming process, it can be automated by using various machine learning and evolutionary computation methods. In previous research, genetic programming has been the most commonly used method for automatically designing new dispatching rules. However, there are many other evolutionary methods that use representations other than genetic programming that can be used to create dispatching rules. Some, such as gene expression programming, have already been used successfully, while others, such as Cartesian genetic programming or grammatical evolution, have not yet been used to generate dispatching rules. In this paper, six different methods (genetic programming, gene expression programming, Cartesian genetic programming, grammatical evolution, stack representation, and analytic programming) for generating dispatching rules for the unrelated machines environment are tested and the results for various scheduling criteria are analysed. It is also analysed how different individual sizes in the tested methods affect the performance and average size of the generated dispatching rules. The results show that, with the exception of grammatical evolution and analytic programming, all tested methods perform quite similarly, with results depending on the selected scheduling criterion. The results also show that Cartesian genetic programming is the most resistant to the occurrence of bloat and evolves dispatching rules with the smallest average size.

Proteins are evolvable in the sense that they are readily able to acquire new or improved functions through the process of mutation and selection. Here, I examine what properties influence the ability of proteins to evolve new functions. I show that proteins with similar biochemical properties can differ substantially in their capacities to withstand mutations and evolve new functions. Specifically, more stable proteins are both more mutationally robust and more evolvable, due to improved tolerance for mutations. This fact can be exploited in protein engineering. I then show how evolutionary theory can be modified to describe how a protein's mutational robustness changes during the normal course of neutral genetic drift. One of the main theoretical predictions is that proteins evolving in larger populations will gain excess stability and mutational robustness, a prediction which I confirm experimentally. Finally, I turn to the question of how neutral genetic drift can alter protein functions that are not under selection. I show that promiscuous functions can change significantly during genetic drift, a phenomenon that may aid in the evolution of beneficial new functions. Overall, this work establishes two mechanisms whereby initially neutral mutations can influence the course of future evolution.

Coevolution fails to maintain genetic variation in a host–parasite model with constant finite population size

Graph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behaviour of Cartesian genetic programming (CGP), linear genetic programming (LGP), evolving graphs by graph programming and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and (1+lambda ). In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods can increase search performance on complex real-world regression problems and, particularly in combination with the (1 + lambda) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP’s number of registers and CGP’s levels back parameter is of utmost importance and contributes significantly to better convergence of an optimization algorithm when solving complex problems that benefit from code reuse.

Cartesian Genetic Programming (CGP) is a type of Genetic Programming, which uses a sequence of integers to represent an executable graph structure. The most common way of optimizing the CGP is to use a simple evolutionary strategy with mutations, which randomly changes the integer values of integer sequence. We propose an alternative genotype-phenotype mapping procedure for CGP allowing usage of real-valued numbers in genotype. Novel representation allows continuous transition between various functions and inputs of each given node (hence the name, Continuous CGP), which means, that the optimization of CGP individual is transformed from combinatorial optimization problem to continuous optimization problem. This allows leveraging various metaheuristic optimization algorithms. In this paper, we present results obtained by Particle Swarm Optimization algorithm, showing that continuous representation is able to outperform classic CGP in some benchmarks and provides competitive results with one of the best performing symbolic regression systems in literature.

Grammatical Evolution (GE) is a genetic programming technique in which the candidate solutions are represented using a binary genotype and the programs can be generated through production rules of a formal grammar. Similarly to other evolutionary computation methods, the GE's performance can be improved when an adequate initial population seeding is adopted. Decision trees are widely used to model classifiers in machine learning and their symbolic form can be mapped back to the GE's binary representation of the candidate individuals. Thus, the use of machine learning techniques to generate decision trees to compose the initial population of GE is investigated here. Computational experiments with a real world data set are carried out and the results show an increase of performance when compared to the traditional seeding approach.

Molecular evolution and epidemiology of echovirus 6 in Finland

Diseases with readily available therapies may eventually prevail against the specific treatment by the acquisition of resistance. The constitutively active Abl1 tyrosine kinase known to cause chronic myeloid leukemia is an example, where patients may experience relapse after small inhibitor drug treatment. Mutations in the Abl1 tyrosine kinase domain (Abl1‐KD) are a critical source of resistance and their emergence depends on the conformational states that have been observed experimentally: the inactive state, the active state, and the intermediate inactive state that resembles Src kinase. Understanding how resistant positions and amino acid identities are determined by selection pressure during drug treatment is necessary to improve future drug development or treatment decisions. We carry out in silico site‐saturation mutagenesis over the Abl1‐KD structure in a conformational context to evaluate the in situ and conformational stability energy upon mutation. Out of the 11 studied resistant positions, we determined that 7 of the resistant mutations favored the active conformation of Abl1‐KD with respect to the inactive state. When, instead, the sequence optimization was modeled simultaneously at resistant positions, we recovered five known resistant mutations in the active conformation. These results suggested that the Abl1 resistance mechanism targeted substitutions that favored the active conformation. Further sequence variability, explored by ancestral reconstruction in Abl1‐KD, showed that neutral genetic drift, with respect to amino acid variability, was specifically diminished in the resistant positions. Since resistant mutations are susceptible to chance with a certain probability of fixation, combining methodologies outlined here may narrow and limit the available sequence space for resistance to emerge, resulting in more robust therapeutic treatments over time.

Leukocyte telomere length (LTL), which reflects telomere length in other somatic tissues, is a complex genetic trait. Eleven SNPs have been shown in genome-wide association studies to be associated with LTL at a genome-wide level of significance within cohorts of European ancestry. It has been observed that LTL is longer in African Americans than in Europeans. The underlying reason for this difference is unknown. Here we show that LTL is significantly longer in sub-Saharan Africans than in both Europeans and African Americans. Based on the 11 LTL-associated alleles and genetic data in phase 3 of the 1000 Genomes Project, we show that the shifts in allele frequency within Europe and between Europe and Africa do not fit the pattern expected by neutral genetic drift. Our findings suggest that differences in LTL within Europeans and between Europeans and Africans is influenced by polygenic adaptation and that differences in LTL between Europeans and Africans might explain, in part, ethnic differences in risks for human diseases that have been linked to LTL.

Yersinia pestis, the agent of bubonic plague, evolved from the enteric pathogen Yersinia pseudotuberculosis within the past 20,000 years. Because ancestor and descendant both exist, it is possible to infer steps in molecular evolution by direct experimental approaches. The Y. pestis life cycle includes establishment of a biofilm within its vector, the flea. Although Y. pseudotuberculosis makes biofilms in other environments, it fails to do so in the insect. We show that rcsA, a negative regulator of biofilms that is functional in Y. pseudotuberculosis, is a pseudogene in Y. pestis. Replacement of the pseudogene with the functional Y. pseudotuberculosis rcsA allele strongly represses biofilm formation and essentially abolishes flea biofilms. The conversion of rcsA to a pseudogene during Y. pestis evolution, therefore, was a case of negative selection rather than neutral genetic drift.

Cartesian Genetic Programming (CGP) is a graph-based genetic programming technique developed by Miller & Thompson in 2000 which has several advantages over the traditional form of tree-based Genetic programming (GP). In the standard form, CGP uses only mutation as a genetic operator whereas the use of traditional crossover operators of GP when applied to CGP led to disrupted results. Various researchers have come out with new genetic operators which when applied to standard CGP or its variants have given better results either in the form of better convergence or efficiency. In this paper, we performed a comparative evaluation of four genetic operators, namely standard crossover, Clegg’s crossover, Graph-based crossover, and forking operator, on standard CGP and tested them on standard benchmark datasets such as Koza-2, Koza-3, and Nguyen-2. Our evaluation shows that both Clegg’s crossover and forking operator performs better than the others.KeywordsSoft computingEvolutionary algorithmGenetic programmingGenetic operators

This paper describes how the process of synthesizing the design of both the topology (control law) and the numerical parameter values (tuning) for a controller can be automated using genetic programming. Genetic programming can be used to automatically make the decisions concerning the total number of signal processing blocks to be employed in a control, the type of each block, the interconnections between the blocks, and the values (tuning) of all parameters for all blocks requiring parameters. In synthesizing the design of controllers, genetic programming can simultaneously optimize prespecified performance metrics (such as minimizing the time required to bring the plant output to the desired value), satisfy time-domain constraints (such as overshoot and disturbance rejection), and satisfy frequency domain constraints. Evolutionary methods have the advantage of not being encumbered by preconceptions that limit its search to well-traveled paths. Genetic programming is applied to an illustrative problem involving the design of a controller for a three-lag plant with a significant (five-second) time delay in the external feedback from the plant to the controller. The delay in the feedback makes the design of an effective controller difficult.

Cartesian Genetic Programming is a form of Genetic Programming based on evolving graph structures. It has a fixed genotype length and a genotype–phenotype mapping that introduces neutrality into the representation. It has been used for many applications and was one of the first Genetic Programming techniques to be implemented on the GPU. In this chapter, we describe the representation in detail and discuss various GPU implementations of it. Later in the chapter, we discuss a recent implementation based on the GPU.net framework.

Classical Evolutionary Programming (CEP) and Fast Evolutionary Programming (FEP) have been applied to real-valued function optimisation. Both of these techniques directly evolve the real-values that are the arguments of the real-valued function. In this paper we have applied a form of genetic programming called Cartesian Genetic Programming (CGP) to a number of real-valued optimisation benchmark problems. The approach we have taken is to evolve a computer program that controls a writing-head, which moves along and interacts with a finite set of symbols that are interpreted as real numbers, instead of manipulating the real numbers directly. In other studies, CGP has already been shown to benefit from a high degree of neutrality. We hope to exploit this for real-valued function optimisation problems to avoid being trapped on local optima. We have also used an extended form of CGP called Embedded CGP (ECGP) which allows the acquisition, evolution and re-use of modules. The effectiveness of CGP and ECGP are compared and contrasted with CEP and FEP on the benchmark problems. Results show that the new techniques are very effective.

Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Operators are represented as trees and are evolved using genetic programming (GP) techniques. The proposed approach is tested with real benchmark functions and an analysis of operator evolution is provided.