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Outlines the development of genetic algorithms (GA), explains how they generate solutions to problems and applies four GA models incorporating different factors (e.g. risk, transaction costs etc.) to financial investment strategies. Uses 1987‐1996 share price data from the Madrid Stock Exchange (Spain) and a buy‐and‐hold strategy in the IBEX‐35 index as a benchmark. Shows that all four GA models generat superior daily returns of long positions with lower risk; and discusses the variations between them in detail.

This study aims to address the challenge of automatic guided vehicle (AGV) scheduling for parcel storage and retrieval in an intelligent warehouse.

This study presents a scheduling solution that aims to minimize the maximum completion time for the AGV scheduling problem in an intelligent warehouse. First, a mixed-integer linear programming model is established, followed by the proposal of a novel genetic algorithm to solve the scheduling problem of multiple AGVs. The improved algorithm includes operations such as the initial population optimization of picking up goods based on the principle of the nearest distance, adaptive crossover operation evolving with iteration, mutation operation of equivalent exchange and an algorithm restart strategy to expand search ability and avoid falling into a local optimal solution. Moreover, the routing rules of AGV are described.

By conducting a series of comparative experiments based on the actual package flow situation of an intelligent warehouse, the results demonstrate that the proposed genetic algorithm in this study outperforms existing algorithms, and can produce better solutions for the AGV scheduling problem.

This paper optimizes the different iterative steps of the genetic algorithm and designs an improved genetic algorithm, which is more suitable for solving the AGV scheduling problem in the warehouse. In addition, a path collision avoidance strategy that matches the algorithm is proposed, making this research more applicable to real-world scheduling environments.

Discusses an ongoing research work which attempts to formulate, develop and test mining equipment reliability assessment models based on genetic algorithms. Genetic algorithms are powerful and broadly applicable stochastic search techniques based on the principles of natural selection, heredity and genetics. The reason for selecting genetic algorithms is the fact that the reliability of mining equipment changes over time due to its dependence upon several covariates/factors (e.g. equipment age, the operating environment, number and quality of repairs). These factors combine to create a complex impact on an equipment’s reliability function. Gives an example of the application of genetic algorithms to capture the impact of these factors on time between failures of a piece of mining equipment.

The Hermite collocation method of discretization can be used to determine highly accurate solutions to the steady‐state one‐dimensional convection‐diffusion equation (which can be used to model the transport of contaminants dissolved in groundwater). This accuracy is dependent upon sufficient refinement of the finite‐element mesh as well as applying upstream or downstream weighting to the convective term through the determination of collocation locations which meet specified constraints. Owing to an increase in computational intensity of the application of the method of collocation associated with increases in the mesh refinement, minimal mesh refinement is sought. Very often this optimization problem is the one where the feasible region is not connected and as such requires a specialized optimization search technique. This paper aims to focus on this method.

An original hybrid method that utilizes a specialized adaptive genetic algorithm followed by a hill‐climbing approach is used to search for the optimal mesh refinement for a number of models differentiated by their velocity fields. The adaptive genetic algorithm is used to determine a mesh refinement that is close to a locally optimal mesh refinement. Following the adaptive genetic algorithm, a hill‐climbing approach is used to determine a local optimal feasible mesh refinement.

In all cases the optimal mesh refinements determined with this hybrid method are equally optimal to, or a significant improvement over, mesh refinements determined through direct search methods.

Further extensions of this work could include the application of the mesh refinement technique presented in this paper to non‐steady‐state problems with time‐dependent coefficients with multi‐dimensional velocity fields.

The present work applies an original hybrid optimization technique to obtain highly accurate solutions using the method of Hermite collocation with minimal mesh refinement.

The purpose of this paper is to propose an accurate product reliability prediction model in order to enhance product quality and reduce product costs.

This study proposes a new method for predicting the reliability of repairable systems. The novel method employed constructs a predictive model by integrating neural networks and genetic algorithms. Findings – The novel method employed constructs a predictive model by integrating neural networks and genetic algorithms. Genetic algorithms are used to globally optimize the number of neurons in the hidden layer, the learning rate and momentum of neural network architecture. Research limitations/implications – This study only adopts real failure data from an electronic system to verify the feasibility and effectiveness of the proposed method. Future research may use other product's failure data to verify the proposed method. The proposed method is superior to ARIMA and neural network model prediction techniques in the reliability of repairable systems. Practical implications – Based on the more accurate analytical results achieved by the proposed method, engineers or management authorities can take follow‐up actions to ensure that products meet quality requirements, provide logistical support and correct product design. Originality/value – The proposed method is superior to other prediction techniques in predicting the reliability of repairable systems.

With the problem of environment and energy becoming prominent, energy conservation and emission reduction have received more attention. In the using process, buildings not only have the inherent energy consumption but also have the energy consumption of equipment that is installed for improving the indoor environment. This study aims to investigate how to reduce the energy consumption of buildings through utilizing natural resources.

This paper briefly introduces three objective functions in the building energy-saving model: building energy consumption, natural lighting and natural ventilation. Genetic algorithm was used to optimize the building parameters to achieve energy conservation and comfort improvement. Then a two-story rental building was analyzed.

The genetic algorithm converged to Pareto optimal solution set after 10,000 times of iterations, which took 61024 s. The lowest energy consumption of the scheme that was selected from the 70 optimal solutions was 5580 W/(m²K), the lighting coefficient was 5.56% and Pressure Difference Pascal Hours (PDPH) was 6453 h; compared with the initial building parameters, the building energy consumption reduced by 3.40%, the lighting coefficient increased by 11.65% and PDPH increased by 9.54%.

In short, the genetic algorithm can effectively optimize the energy-saving parameters of buildings.

This study aims to present a novel Genetic Algorithm-Based Design Model (GABDM) to provide reduced-risk areas, namely, a “safe footprint,” in interior spaces during earthquakes. This study focuses on housing interiors as the space where inhabitants spend most of their daily lives.

The GABDM uses the genetic algorithm as a method, the Nondominated Sorting Genetic Algorithm II algorithm, and the Wallacei X evolutionary optimization engine. The model setup, including inputs, constraints, operations and fitness functions, is presented, as is the algorithmic model’s running procedure. Following the development phase, GABDM is tested with a sample housing interior designed by the authors based on the literature related to earthquake risk in interiors. The implementation section is organized to include two case studies.

The implementation of GABDM resulted in optimal “safe footprint” solutions for both case studies. However, the results show that the fitness functions achieved in Case Study 1 differed from those achieved in Case Study 2. Furthermore, Case Study 2 has generated more successful (higher ranking) “safe footprint” alternatives with its proposed furniture system.

This study presents an original approach to dealing with earthquake risks in the context of interior design, as well as the development of a design model (GABDM) that uses a generative design method to reduce earthquake risks in interior spaces. By introducing the concept of a “safe footprint,” GABDM contributes explicitly to the prevention of earthquake risk. GABDM is adaptable to other architectural typologies that involve footprint and furniture relationships.

Offshore structures are among the structures exposed to fire more often. Most of these structures are likely to be associated with flammable materials. In this research, some of the structures constructed on top of marine decks have been studied.

For this purpose, the upper-bound theory of plastic analysis has been used to investigate its collapse behavior. In this way, genetic algorithm has been used for application of the combination of elementary mechanisms in the classic plastic analysis problem.

The studied structures are optimized by plastic analysis theory before and after the fire and their failure modes are compared with each other. The comparison of the results indicates significant changes in the load factor value, as well as the critical collapse mode of the structure before and after the fire.

Results indicate that the combination of plastic analysis and a genetic algorithm can predict the collapse mode of the structure before and after the fire accurately.

The purpose of this work is to study the capability of heuristic algorithms like genetic algorithm to estimate the electron transport parameters of the Gallium Arsenide (GaAs). Also, the paper provides a simple but complete electron mobility model for the GaAs based on the genetic algorithm that can be suitable for use in simulation, optimization and design of GaAs‐based electronic and optoelectronic devices.

The genetic algorithm as a powerful heuristic optimization technique is used to approximate the electron transport parameters during the model development.

The capability of the model to approximate the electron transport properties of Gallium Arsenide is tested using experimental and Monte Carlo data. Results show that the genetic algorithm based model can provide a reliable estimate of the electron mobility in Gallium Arsenide for a wide range of temperatures, concentrations and electric fields. Based on the obtained results, this paper shows that the genetic algorithm can be a useful tool for the estimation of the transport parameters of semiconductors.

For the first time, the genetic algorithm is used to calculate the electron transport parameters in Gallium Arsenide. A complete electron mobility model for a wide range of temperatures, doping concentrations, compensation ratios and electric fields is developed.