Constraint Handling in Genotype to Phenotype Mapping and Genetic Operators for Project Staffing

Project staffing in many organisations involves the assignment of people to multiple projects while satisfying multiple constraints. The use of a genetic algorithm with constraint handling performed during a genotype to phenotype mapping process provides a new approach. Experiments show promise for this technique

Using a Variational Autoencoder to Learn Valid Search Spaces of Safely Monitored Autonomous Robots for Last-Mile Delivery

The use of autonomous robots for delivery of goods to customers is an exciting new way to provide a reliable and sustainable service. However, in the real world, autonomous robots still require human supervision for safety reasons. We tackle the real-world problem of optimizing autonomous robot timings to maximize deliveries, while ensuring that there are never too many robots running simultaneously so that they can be monitored safely. We assess the use of a recent hybrid machine-learning-optimization approach COIL (constrained optimization in learned latent space) and compare it with a baseline genetic algorithm for the purposes of exploring variations of this problem. We also investigate new methods for improving the speed and efficiency of COIL. We show that only COIL can find valid solutions where appropriate numbers of robots run simultaneously for all problem variations tested. We also show that when COIL has learned its latent representation, it can optimize 10% faster than the GA, making it a good choice for daily re-optimization of robots where delivery requests for each day are allocated to robots while maintaining safe numbers of robots running at once

Genetic algorithms for multiple-choice problems

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems. It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success. Two multiple-choice problems are considered. The first is constructing a feasible nurse roster that considers as many requests as possible. In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income. Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems. However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework. Hence, the main theme of this work is to balance feasibility and cost of solutions. In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches. The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis. To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions. As well as a theoretical discussion as to the underlying reasons for using each operator, extensive computational experiments are carried out on a variety of data. These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality. The most successful variant of our algorithm has a more than 99% chance of finding a feasible solution which is either optimal or within a few percent of optimality

Refinement and standardization of storage procedures for clonal crops. Global Public Goods Phase 2: Part 1. Project landscape and general status of clonal crop in vitro conservation technologies

Among the collective actions of the World Bank-funded Global Public Goods Phase II Project (GPG2), the following collaborative activity: “Refinement and standardization of storage procedures for clonal crops” was given to the CGIAR’s In Vitro Genebanks, represented by the Clonal Crop Task Force (CCTF) composed of genetic resources research staff from the four centres: Bioversity International, CIAT, CIP and IITA. These hold the in trust collections of Musa, cassava, potato, sweetpotato, yam and Andean root and tuber crops (ARTCs). The overarching aims of this activity were to: (1) review the status of vitro conservation in the context of the GPG2 project with an emphasis on the mandated clonal crops; (2) survey the facilities, storage protocols and practices of CGIAR’s clonal crop genebanks; (3) collate and review this information with a view to developing quality and risk management systems to support the production and validation of multi-crop best practice guidelines. Outputs from this activity are designated as a three part ‘trilogy’: Part I, entitled “Project landscape and general status of clonal crop in vitro conservation technologies” introduces the GPG2 project within the CGIAR landscape and overviews the status of in vitro plant conservation in the wider conservation community of practice. This part describes the role of risk and quality management for the effective maintenance of in vitro genebanks in the context of research and the development and validation of best practices

Determining optimal police patrol deployments: a simulation-based optimisation approach combining agent-based modelling and genetic algorithms

One of the most important tasks faced by police agencies concerns the strategic deployment of patrols in order to respond to calls whilst also deterring crime. Current deployment strategies typically lack robustness as they are often based on tradition. As police agencies are encouraged to improve the effectiveness and efficiency of their services, it is essential to devise advanced patrol deployments that are based on recent scientific evidence. Most existing models of patrol deployments are too simplistic, and are thus unable to provide a realistic representation of the complexity of patrol activities. Furthermore, past studies have tended to focus on individual aspects of patrol deployment such as efficiency, reactive effectiveness or proactive effectiveness, rather than consider them all together as part of the same problem. This thesis proposes to develop a decision-support tool for informing better patrol deployment designs. This tool consists of a simulation-based optimisation approach combining two key components: (1) an agent-based model (ABM) of patrol activities used to evaluate the performance of the system under a given deployment configuration and (2) a genetic algorithm (GA) which seeks to speed up the search for optimal deployments. While the developed framework is designed to be applicable to any police force, a case study is provided for the city of Detroit in order to demonstrate its potential. The developed decision-support tool shows considerable potential in informing more cost-effective patrol deployments. First, the ABM of patrol activities allows for exploration of the impact of various deployment decisions that police agencies are unable to experiment with in the real world. Second, the GA makes it possible to optimise patrol deployments by identifying 'good' solutions, which provide faster responses to incidents and deter crime in key areas, in reasonable time

Plant breeding and farmer participation

This book complements the traditional approach to plant breeding by addressing a number of issue specifically related to the participation of farmers in a plant breeding programme, and provides a comprehensive description and assessment of the use of participatory plant breeding in developing countries. It is aimed at plant breeders, social scientists, students and practitioners interested in learning more about its use, with the hope that they all will find a common ground to discuss ways in which plant breeding can be beneficial to all and can contribute to alleviate poverty

A critical review of the current global ex situ conservation system for plant agrobiodiversity. II. Strengths and weaknesses of the current system and recommendations for its improvement

In this paper, we review gene bank operations that have an influence on the global conservation system, with the intention to identify critical aspects that should be improved for optimum performance. We describe the role of active and base collections and the importance of linking germplasm conservation and use, also in view of new developments in genomics and phenomics that facilitate more effective and efficient conservation and use of plant agrobiodiversity. Strengths, limitations, and opportunities of the existing global ex situ conservation system are discussed, and measures are proposed to achieve a rational, more effective, and efficient global system for germplasm conservation and sustainable use. The proposed measures include filling genetic and geographic gaps in current ex situ collections; determining unique accessions at the global level for long-term conservation in virtual base collections; intensifying existing international collaborations among gene banks and forging collaborations with the botanic gardens community; increasing investment in conservation research and user-oriented supportive research; improved accession-level description of the genetic diversity of crop collections; improvements of the legal and policy framework; and oversight of the proposed network of global base collections