Optimization Aspects of Carcinogenesis

Any process in which competing solutions replicate with errors and numbers of their copies depend on their respective fitnesses is the evolutionary optimization process. As during carcinogenesis mutated genomes replicate according to their respective qualities, carcinogenesis obviously qualifies as the evolutionary optimization process and conforms to common mathematical basis. The optimization view accents statistical nature of carcinogenesis proposing that during it the crucial role is actually played by the allocation of trials. Optimal allocation of trials requires reliable schemas' fitnesses estimations which necessitate appropriate, fitness landscape dependent, statistics of population. In the spirit of the applied conceptual framework, features which are known to decrease efficiency of any evolutionary optimization procedure (or inhibit it completely) are anticipated as "therapies" and reviewed. Strict adherence to the evolutionary optimization framework leads us to some counterintuitive implications which are, however, in agreement with recent experimental findings, such as sometimes observed more aggressive and malignant growth of therapy surviving cancer cells

An Investigation into the Merger of Stochastic Diffusion Search and Particle Swarm Optimisation

This study reports early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) to the Particle Swarm Optimiser (PSO) metaheuristic, effectively merging the two swarm intelligence algorithms. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between particles, has the potential to improve the optimisation capability of conventional PSOs

The evolution of a visual-to-auditory sensory substitution device using interactive genetic algorithms

Sensory Substitution is a promising technique for mitigating the loss of a sensory modality. Sensory Substitution Devices (SSDs) work by converting information from the impaired sense (e.g. vision) into another, intact sense (e.g. audition). However, there are a potentially infinite number of ways of converting images into sounds and it is important that the conversion takes into account the limits of human perception and other user-related factors (e.g. whether the sounds are pleasant to listen to). The device explored here is termed “polyglot” because it generates a very large set of solutions. Specifically, we adapt a procedure that has been in widespread use in the design of technology but has rarely been used as a tool to explore perception – namely Interactive Genetic Algorithms. In this procedure, a very large range of potential sensory substitution devices can be explored by creating a set of ‘genes’ with different allelic variants (e.g. different ways of translating luminance into loudness). The most successful devices are then ‘bred’ together and we statistically explore the characteristics of the selected-for traits after multiple generations. The aim of the present study is to produce design guidelines for a better SSD. In three experiments we vary the way that the fitness of the device is computed: by asking the user to rate the auditory aesthetics of different devices (Experiment 1), by measuring the ability of participants to match sounds to images (Experiment 2) and the ability to perceptually discriminate between two sounds derived from similar images (Experiment 3). In each case the traits selected for by the genetic algorithm represent the ideal SSD for that task. Taken together, these traits can guide the design of a better SSD

Resource allocation and scheduling of multiple composite web services in cloud computing using cooperative coevolution genetic algorithm

In cloud computing, resource allocation and scheduling of multiple composite web services is an important and challenging problem. This is especially so in a hybrid cloud where there may be some low-cost resources available from private clouds and some high-cost resources from public clouds. Meeting this challenge involves two classical computational problems: one is assigning resources to each of the tasks in the composite web services; the other is scheduling the allocated resources when each resource may be used by multiple tasks at different points of time. In addition, Quality-of-Service (QoS) issues, such as execution time and running costs, must be considered in the resource allocation and scheduling problem. Here we present a Cooperative Coevolutionary Genetic Algorithm (CCGA) to solve the deadline-constrained resource allocation and scheduling problem for multiple composite web services. Experimental results show that our CCGA is both efficient and scalable

An Introduction to Temporal Optimisation using a Water Management Problem

Optimisation problems usually take the form of having a single or multiple objectives with a set of constraints. The model itself concerns a single problem for which the best possible solution is sought. Problems are usually static in the sense that they do not consider changes over time in a cumulative manner. Dynamic optimisation problems to incorporate changes. However, these are memoryless in that the problem description changes and a new problem is solved - but with little reference to any previous information. In this paper, a temporally augmented version of a water management problem which allows farmers to plan over long time horizons is introduced. A climate change projection model is used to predict both rainfall and temperature for the Murrumbidgee Irrigation Area in Australia for up to 50 years into the future. Three representative decades are extracted from the climate change model to create the temporal data sets. The results confirm the utility of the temporal approach and show, for the case study area, that crops that can feasibly and sustainably be grown will be a lot fewer than the present day in the challenging water-reduced conditions of the future

Using numerical plant models and phenotypic correlation space to design achievable ideotypes

Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modeling approach, which identified paths for desirable trait modification, including direction and intensity.Comment: 25 pages, 5 figures, 2017, Plant, Cell and Environmen