Genetic Algorithms In Machine Learning Research Articles

Genetic Algorithms Application for Pricing Optimization in Commodity Markets

The perishable nature of vegetable commodities poses challenges for superstores, as reselling them is often unfeasible due to their short freshness period. Reliable market demand analysis is crucial for boosting revenue. This study simplifies the pricing and replenishment decision-making process by making reasonable assumptions about the selling time, wastage rate, and replenishment time for vegetable commodities. A single-objective planning model with the objective of profit maximization was constructed by fitting historical data using the nonparametric method of support vector regression (SVR). The study reveals a specific relationship between sales volume and cost-plus pricing for each category and predicts future cost changes using an LSTM model. Combining these findings, we substitute the relationship between sales volume and pricing as well as the LSTM prediction data into the model, and solve it using genetic algorithms in machine learning to derive the optimal replenishment volume and pricing strategy. Practical results show that the method can provide reasonable pricing and replenishment strategies for vegetable superstores, and after careful accounting, we arrive at an expected profit of RMB 22,703.14. The actual profit of the supermarket was RMB 19,732.89. The method, therefore, increases the profit of the vegetable superstore by 13.08%. By optimizing inventory management and pricing decisions, the superstore can better meet the challenges of vegetable commodities and achieve sustainable development.

Genetic optimization of asteroid families’ membership

Asteroid families are groups of asteroids with a common origin, such as prior collisions or the parent body’s rotational fission. In proper [a, e, sin(i)] element domains, they are generally observed using the hierarchical clustering technique (HCMs), but the method may be ineffective in high-density regions, where it may be unable to separate near families. Previous works employed a different technique in which nine different machine learning classification algorithms were applied to the orbital distribution in proper elements of 21 known family constituents for the goal of new members’ identification. Each algorithm’s optimal hyper-parameters for every family were extensively investigated, which proved to be a time-consuming and repetitive procedure. Herein, we used a genetic algorithm-based tool to identify the most optimal machine learning algorithm for the same studied asteroid families as an alternative to the originally utilized parameter search mode. When compared to the same evaluative metrics utilized in the previous machine learning application study, the precision values of the new genetic machine learning algorithms have been consistently comparable, demonstrating that this alternative technique can be satisfactorily efficient and fast.

Redrawing of a Housing Market: Insurance Payouts and Housing Market Recovery in the Wake of the Christchurch Earthquake of 2011

On the 22nd of February 2011, much of the residential housing stock in the city of Christchurch, New Zealand, was damaged by an unusually destructive earthquake. Almost all of the houses were insured. We ask whether insurance was able to mitigate the damage adequately, or whether the damage from the earthquake, and the associated insurance payments, led to a spatial re-ordering of the housing market in the city. We find a negative correlation between insurance pay-outs and house prices at the local level. We also uncover evidence that suggests that the mechanism behind this result is that in some cases houses were not fixed (i.e., owners having pocketed the payments) - indeed, insurance claims that were actively repaired (rather than paid directly) did not lead to any relative deterioration in prices. We use a genetic machine-learning algorithm which aims to improve on a standard hedonic model, and identify the dynamics of the housing market in the city, and three data sets: All housing market transactions, all earthquake insurance claims submitted to the public insurer, and all of the local authority’s building-consents data. Our results are important not only because the utility of catastrophe insurance is often questioned, but also because understanding what happens to property markets after disasters should be part of the overall assessment of the impact of the disaster itself. Without a quantification of these impacts, it is difficult to design policies that will optimally try to prevent or ameliorate disaster impacts.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks.

An Evolutionary Approach to Improve Connectivity Prediction in Mobile Wireless Sensor Networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

Genetic algorithms in machine learning

Genetic algorithms in machine learning