Implementation Of Genetic Algorithm Research Articles

Enhancing Oil Recovery in Eagle Ford Shale: A Multiscale Simulation Study of Surfactant Huff ’n’ Puff Methodology

In this paper, we present a simulation case study of a surfactant huff ’n’ puff pilot in the black oil window of the Eagle Ford (EF) Shale. The target horizontal well, which had been depleted for nearly 8 years, underwent stimulation via a surfactant huff ’n’ puff treatment. The surfactant was selected through laboratory screening using reservoir rock and fluid samples. After a 17-hour injection and a 1-month shut-in period, the well’s production increased fivefold from the baseline oil rate, sustaining incremental oil production for at least 2 years. The surfactant enhances oil recovery by altering rock wettability toward a more water-wet state and moderating oil/water interfacial tension (IFT). This process is modeled by surfactant adsorption in the simulator, indicating the degree of dynamic changes in relative permeability (krl) and capillary pressure (Pc) curves. We propose a comprehensive workflow comprising three stages: development of core-scale and field-scale models, sequential model calibrations, and multiobjective optimization to integrate laboratory measurements and field data from this pilot into multiscale numerical simulations. By matching oil recoveries from imbibition experiments on the core model and field production histories on the field model, krl and Pc profiles of two extreme states, basic reservoir properties, and additional reservoir properties altered during huff ’n’ puff operations are characterized. The matched core model reproduces a 15.1% incremental oil recovery for surfactant-assisted spontaneous imbibition (SASI) process relative to pure brine imbibition process. The matched reservoir model predicts the surfactant huff ’n’ puff treatment increases the oil production by 21.9% relative to water huff ’n’ puff treatment and by 52.9% relative to primary depletion for a 4-year period. The calibrated reservoir model also serves as a base case for optimizing well operation schedules through the implementation of a multiobjective genetic algorithm. The surfactant injection rate, injection time, and well shut-in time of the base case are varied to achieve higher oil production and reduced surfactant usage. Statistical analysis of eight trade-off cases indicates that optimal well operations, compared with existing practices, frequently involve increased injection rates [16.6–18.9 barrels per minute (bpm)], shorter injection periods (10–11.3 hours), and prolonged shut-indurations (49–65 days). This workflow offers valuable insights into surfactant huff ’n’ puff treatments for unconventional reservoirs, thereby facilitating the optimization of well operations and maximizing tertiary oil recovery.

Multi-objective optimization of cable-road layouts in smart forestry

ABSTRACT Current cable-road layouts for timber harvesting in steep terrain are often based on either manual planning or automated layouts generated from low-resolution GIS data, limiting potential benefits and informed decision-making. In this paper, we present a novel approach to improve cable-road design using multi-objective optimization based on realistic cable-road representations. We systematically compare the effectiveness of single-objective and multi-objective optimization methods for generating layouts using these representations. We implement and evaluate the performance of a weighted single-objective approach, the AUGMECON2 and NSGA-II multi-objective methods in comparison to a layout manually created by a forestry expert, taking into account installation costs, harvesting volumes, residual stand damage and lateral yarding workload. In addition to implementing the first linear programming multi-objective optimization for realistic cable-road representations by adapting AUGMECON2, we also present the first implementation of a multi-objective genetic algorithm (NSGA-II) with simulated annealing for this purpose and evaluate their respective strengths. We find that the use of multi-objective optimization provides advantages in terms of cost-effective, balanced and adaptable cable-road layouts while allowing economic and environmental considerations to be incorporated into the design phase.

Improving genetic testing utilization in a tertiary care neonatal intensive care unit through quality improvement.

There is an increasing recognition of the importance of diagnosing genetic conditions with an ever-growing list of genetic testing options. However, most providers do not have formal genetics training, which makes choosing the most appropriate test to order challenging. Our project sought to improve cytogenetic testing utilization in a tertiary care neonatal intensive care unit (NICU) through utilizing quality improvement techniques, specifically the Model for Improvement framework with rapid Plan-Do-Study-Act cycles. Our project utilized various interventions including the implementation of a NICU genetic testing algorithm. Interventions demonstrated improvement in all areas, specifically a 92% reduction in unnecessary cytogenetic testing with improvement in the diagnostic rate. Our work also resulted in a 59% decrease in charges with an estimated projected savings of $21,000 per year. Quality improvement can minimize redundancies and inefficiencies in genetic testing in a Level IV NICU in a large tertiary care children's hospital and result in substantial cost-savings.

Comparative Analysis of Metaheuristic Algorithms for Procedural Race Track Generation in Games

Procedural Content Generation (PCG) aims to automatically generate the content of games using algorithmic approaches, as this can reduce the cost of game design and development. PCG algorithms can be applied to all elements of a game, including terrain, maps, stories, dialogues, quests, and characters. A wide variety of search algorithms can be applied to PCG problems; however, those most often used are variations of evolutionary algorithms. This study focuses on comparing three metaheuristic approaches applied to racetrack games, with the specific goal of evaluating the effectiveness of different algorithms in producing game content. To that end, a Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) are applied to a game-level design task to attempt to identify any discernible differences in their performance and identify whether alternative algorithms offer desirable performance characteristics. The results of the study indicate that both the ABC and PSO approaches offer potential advantages to Genetic Algorithm implementation.

MODIFICATION OF A QUANTUM-INSPIRED GENETIC ALGORITHM FOR NUMERICAL OPTIMIZATION USING QUDIT UNDER CONDITIONS OF SIMULATING QUANTUM DECOHERENCE

The genetic algorithm for numerical optimization (GA) of the metaheuristic class is a method for finding optimal solutions based on the biological principles of natural selection and variability. GA is characterized by high operating speed, resistance to noise in the data, low probability of hitting the local extremum of the multimodal objective function, as well as the simultaneous application of probabilistic and deterministic rules for generating search space points. An alternative to the classical GA is the quantum-inspired genetic algorithm for numerical optimization (QIGA), which has advantages that are unattainable for GA by using the concepts and principles of quantum computing. The article proposes a new approach to the implementation of a quantum-inspired genetic numerical optimization algorithm for searching for the global maximum of the objective function, based on modeling the functioning of the GA by simulating the execution of quantum calculations based on qudit in the conditions of the existence of quantum decoherence in the era of noisy medium-scale quantum algorithms. For this purpose, to carry out quantum operations of rotating the states of multilevel quantum systems, the paper presents a density matrix based on Heisenberg-Weyl operators as an analogue of the Bloch sphere for qudits. The simulation of quantum decoherence is interpreted from the point of view of the influence of extraneous noise emanating from the environment on the qudit and is presented as the use of a normal random variable with zero mathematical expectation and unit variance in quantum gates. At the same time, the work presents detailed pseudocodes of the functioning of both the most modified quantum-inspired genetic algorithm for numerical optimization and its individual operations. Testing is carried out by conducting computational experiments with the implementation of a modified algorithm on two-dimensional and multidimensional functions of test optimization problems, as well as when solving an applied optimization problem of planning hybrid flow production in the manufacturing industry based on financial costs and solving the problem of increasing forecasting accuracy based on compact extreme learning machines. The experimental results demonstrate the superiority of the new algorithm over QIGA and classical optimization algorithms in the accuracy of the solution, the speed of convergence with the target value of the global maximum and the execution time of the algorithm.

Container drayage problem integrated with truck appointment system and separation mode

Growing demand for seaborne trade has exacerbated the challenges faced by drayage operators in inland container transportation. In some cases, drayage operators must make truck scheduling decisions and book periods for trucks to visit terminals daily. This paper considers the appointment system and separation mode in container drayage problem, which consists of delivery and pickup transportation tasks for empty and full containers. A mixed-integer linear programming model is developed to minimize the total truck operating time while solving the truck scheduling problem, the empty container allocation problem, and the appointment problem. Another key feature of the model is considering the temporal constraints between tasks from a more realistic perspective. Due to the complexity of the problem, it is not practical to obtain exact solutions for large instances. Therefore, we complete the implementation of a new genetic algorithm (GA) that introduces a topological sort tailored to this problem. Several experiments and analyses are used to demonstrate the correctness of the proposed model as well as the efficiency of the algorithm. The findings indicate that, when compared to traditional mode, separation mode can reduce total truck operating time by an average of 29.4%. The total truck operating time difference between the two modes shows an overall positive trend if the task size increases. In addition, a rational setting of the appointment system will have a beneficial effect on truck scheduling. The decision on the selection of periods and the increment of each quota is crucial in optimizing the setting of the appointment system to reduce the total truck operating time for the mutual benefit of both terminals and drayage operators.

Cost and Density Evaluation Function Application, for Optimal Biodiesel Mixtures by Genetic Algorithm Implementation

The current document presents a fresh method for addressing the optimization challenges concerning fuel mixtures in the production of Biodiesel. Given the rising concerns over diesel emissions and the associated expenses, there's a growing interest in exploring alternative fuel options. Traditional desulphurization methods are time-consuming and require substantial financial investments. Conversely, Biodiesel offers a promising solution as it's derived from renewable resources and is environmentally sustainable. This study introduces an enhanced genetic algorithm that assesses the proportions of components within a fuel mixture blend, aiming to create optimal combinations for Biodiesel production. Apart from cost considerations, the density of the fuel, a key physicochemical characteristic, is pivotal in determining its suitability for widespread use and commercialization. Rigorous experimentation has resulted in highly precise Biodiesel blends, suggesting an optimal fuel solution for each specific set. For instance, in Set 1, Biodiesel was composed of 75.031% diesel and 24.969% biodiesel, with a mixture cost of 1.6975 €/l and a density of 0.8355 g/ml. In Set 2, the fuel mixture consisted of 75.016% diesel and 24.984% biodiesel, with a cost of 1.6977 €/l and a density of 0.8366 g/ml. Notably, the new Biodiesel fuels are significantly cheaper, costing 15.13% less (Set 1) and 15.12% less (Set 2) than diesel (priced at 2.0000 €/l) and are proposed between 1.5 * 109 evaluated biodiesel mixtures.

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Purpose. To develop a method for synthesizing the structure and algorithm of the system for automated control of power consumption by up to 1000 V electrical receivers at mining enterprises with iron ore underground mining methods. This enables direct control of the load connection to the industrial power grid to ensure minimum power costs depending on its cost per day ahead. Methodology. The problem of controlling power consumption of electrical receivers at iron ore underground mines is formalized as a binary form of mixed integer programming. To solve it, a binary implementation of the heuristic genetic algorithm is used. The mathematical modeling method analyzes the impact of genetic algorithm settings, such as the number of phenotypes in the population, the number of elite phenotypes that pass unchanged to the next generation, and the method of phenotype crossover on its quality. Findings. As a result of the research, it is found that the most effective way to control the process of power consumption based on an evolutionary genetic algorithm is to use the Laplace crossover function and keep the percentage of elite phenotypes in the population at 10 %. Moreover, at the smallest population size, the best accuracy is observed when using the Laplace function, while at one- and two-point crossover functions, it worsens, but not significantly (no more than 0.2 %). However, as the number of elite phenotypes increases, the duration of the evolutionary search in the control process is reduced by almost a factor of two in the case of one- and two-point crossovers. Originality. For the first time, the structure of a heuristic system for automated control of power consumption by underground electrical receivers with a supply voltage of up to 1000 V at iron ore underground mines has been developed on the basis of an evolutionary genetic algorithm. Depending on the designed volumes of ore production and the daily power cost per day, this allows determining the optimal power load schedule of underground distribution substations in advance, which, subject to the accepted limits on hourly and daily power, minimizes the cost of purchasing power, and thus reduces the cost of the final product. Practical value. The architecture of a heuristic system for controlling power consumption by electrical receivers with a voltage of up to 1000 V based on an evolutionary genetic algorithm is developed and recommended when optimizing the power load schedule of transformer substations of mining and metallurgical enterprises, in particular, of iron ore underground mines operating in this voltage class.

Heat transfer optimization of uo2-mo fuel using genetic algorithms

Two genetic algorithm (GA) methods were applied to thermal finite element models to optimize the heat transfer efficacy of a UO2-Mo composite fuel pellet with typical pressurized water reactor fuel geometry. Mo additions to UO2 have been shown to increase the thermal conductivity, thus reducing centerline temperatures and temperature gradients. Previous studies evaluated uniformly dispersed Mo or continuous Mo internal geometries (e.g., fins, plates, discs) that were selected using engineering intuition. The current study uses two different implementations of the same GA to optimize Mo placement and minimize the fuel temperature with the only constraint being a maximum 10% Mo volume fraction. One approach superimposed Mo line elements onto the monolithic UO2 pellet model, and the other converted entire UO2 volume elements to Mo. The former method generated 1D heat transfer connections between nodes, whereas the latter method allowed for the formation of 3D structures. Features of the optimal fuel design produced by the GAs included dispersed Mo near the centerline that shifted the peak fuel temperature outward by 0.6 mm, Mo chains in the high-heat-flux region in the mid-to-outer radial zone, and a large continuous structure that spanned the full radius and height of the pellet and accounted for 87.7 % of the total Mo in the pellet. Analysis of this design indicates that the optimal Mo configuration is a balance between creating continuous heat transfer pathways and optimally dispersing Mo to minimize the heat transfer distance through UO2. This architecture ultimately produced an effective thermal conductivity of 11.3 W/m·K under the assumed boundary conditions. This result is higher than any previous values from the literature. Potential fabrication methods and challenges are discussed in addition to the implications on fuel performance.

Implementation of Genetic Algorithm to generate Backstepping Controller’s gains for MPPT of Partially Shaded Photovoltaic Panels

Implementation of Genetic Algorithm to generate Backstepping Controller’s gains for MPPT of Partially Shaded Photovoltaic Panels

Implementation of Genetic Algorithm for Optimal Power Flow with Thyristor-controlled series compensator

Implementation of Genetic Algorithm for Optimal Power Flow with Thyristor-controlled series compensator

Implementation of Genetic Algorithm on Vehicle Routing System

Abstract: This study explores the use of Genetic Algorithms (GAs) to solve the Vehicle Routing Problem (VRP) in logistics. GAs, inspired by evolutionary principles, prove effective in finding efficient routes for a fleet of vehicles serving customers where the goal is to find the most efficient routes for a fleet of vehicles serving customers. Inspired by evolution, GAs prove effective in this task by selecting promising routes, creating new options through crossover and mutation, and evaluating fitness based on objectives and constraints. The study incorporates techniques like reparation, penalties, and adaptive tuning to boost GA performance. Real-world applications in transportation and logistics demonstrate substantial cost savings. The key takeaway is the crucial role of parameter selection, representation design, and fitness function formulation in the success of GAs in solving VRP. The combination of GAs and VRP resolution shows promise in optimizing logistics, improving efficiency, and enhancing service quality. In simpler terms, the study explores a smart way, inspired by nature, to figure out the best routes for delivery trucks, leading to significant cost savings and better service.

A novel partition strategy for efficient implementation of 3D Cellular Genetic Algorithms

Solving optimization problems while fulfilling real-time constraints requires high algorithmic and processing performance. Cellular Genetic Algorithms (cGAs) have been competitive at difficult single objective combinatorial and continuous domain problems. Moreover, it has been demonstrated that structural properties in cGAs, such as population topology dimension, local neighborhood configuration and ad-hoc selection mechanisms, allow not only further algorithmic improvement but also, these characteristics can be combined at hardware level for acceleration. In this article, a novel partition strategy to exploit 3D cGAs population dynamics on a 2D processing array using Field Programmable Gate Arrays (FPGAs) as the target processing platform is presented. The proposed architecture fits as an optimization module within an embedded system where real-time constraints must be fulfilled. Therefore, it is important to find an optimal trade-off between hardware resources usage and searching time. Overall results demonstrate that the proposed architecture can run up to 90 MHz when tackling continuous benchmark functions. Moreover, speed-up of up to three and two orders of magnitude are achieved in comparison to a single CPU and a parallel GPU respectively.

Evaluating Alternative Metaheuristic Algorithms for Procedural Content Generation in Game Design

Procedural Content Generation (PCG) has emerged as a powerful approach for automating game content creation, offering significant benefits in terms of cost reduction and time efficiency compared to traditional game design and development processes (Zhang, Zhang, & Huang, 2022). While Genetic Algorithms (GAs) have been widely used in PCG, alternative metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) have demonstrated their effectiveness in delivering high-quality solutions and efficient optimization capabilities across different problem domains (Amato, 2017). However, their application in PCG remains limited. I aim to evaluate the performance of PSO and ABC in map layout generation, challenging the conventional use of GAs. By comparing three metaheuristic algorithms (GA, ABC, and PSO) I seek to assess the effectiveness of these approaches in generating game levels and identify any obvious differences in their performance characteristics. Comprehensive experiments are conducted, applying GA, ABC, and PSO to a map layout generation. Metrics like convergence speed and content quality are used to evaluate the generated game content. My findings reveal that both ABC and PSO demonstrate advantages over traditional GA implementations when generating game levels, indicating their potential for enhancing PCG. In this presentation, I will share the results of comparing three metaheuristic algorithms (GA, PSO, and ABC) in map layout generation for game levels, emphasizing the potential benefits of leveraging diverse algorithmic approaches to create more captivating and immersive game worlds. Also, I will conclude with a call for further research in this area to expose new possibilities in content generation. By considering varied metaheuristic approaches, game developers can improve content generation techniques and create more captivating and interactive player experiences.

Proof of Evaluation-based energy and delay aware computation offloading for Digital Twin Edge Network

The increasing availability of Internet of Things (IoT) applications has led to the development of new technologies. Specifically, the deployment of edge servers close to IoT devices has strengthened the edge computing paradigm. With the collaboration of Mobile Edge Computing (MEC) and cloud computing, delay-sensitive and computation-intensive tasks can be offloaded to the edge/cloud servers to improve system performance in terms of the delay and energy consumption of IoT devices. However, there is a need to schedule the computation tasks for an efficient management. More importantly, the task scheduling strategy can face data tampering attacks to deliberately modify, destroy or manipulate the decisions. To solve the above problems, in this paper, we newly propose to integrate digital twin and blockchain into the edge networks. However, it is unclear (i) how energy and delay-aware computation should be combined, and (ii) which mining computations should be executed for a secure task scheduling. The state-of-the-art focuses on task scheduling and blockchain mining, separately. Therefore, we propose a novel blockchain-based digital twin-edge network architecture where our proposed algorithm solves these two challenges at the same execution. We design a three-layer system architecture, composed of physical entity layer, digital twin edge layer and blockchain layer. In the physical entity layer, we formulate an energy and delay-aware task scheduling problem. In the digital twin edge layer, we propose a novel Proof of Evaluation (PoE)-based secure energy and delay-aware task scheduling algorithm where optimization is executed by the genetic algorithm implementation of Warehouse Location Problem (WLP). In the blockchain layer, the best-found solutions are shared with the topology in a blockchain. Here, each block includes the hash of the previous block, a genetic algorithm-based solution, nonce value, and a hash of whole blocks in the blockchain. Thus, we aim to execute the computation tasks with an acceptable delay in an energy-efficient manner and prevent data tampering attacks against the optimal computation decisions. We validate the outcomes of our PoE-based secure digital twin-edge network model with extensive evaluations. Since the proposed model distributes the task not only to the local device but also to the MEC and cloud server for delay awareness, it reduces the delay but consumes more energy. Nevertheless, the additional energy consumption can be neglected against the delay reduction. The proposed scheme is also more scalable to compare with the conventional solution. The numerical results clearly show that the proposed model provides energy and delay awareness, maintaining both data integrity and trustworthiness at the same execution of algorithm.

РОЗВ’ЯЗАННЯ ЗАДАЧ ОПТИМІЗАЦІЇ В ЕНЕРГЕТИЦІ ЗА ДОПОМОГОЮ ГЕНЕТИЧНОГО АЛГОРИТМУ

The article discusses the application of genetic algorithms in the field of energy optimization. Linear programming is commonly used for optimization problems in energy systems. Linear programming is a mathematical optimization method that seeks the optimal solution under constraints, where all constraints and the objective function are linear functions. In the realm of artificial intelligence,genetic algorithms are employed for optimization tasks. genetic algorithms mimic natural evolution processes, including selection, crossover, mutation, and adaptation, to solve optimization and search problems. The article outlines the process of a genetic algorithm, starting with the formation of an initial population and proceeding through selection, crossover, mutation, and evaluation. This cycle repeats until an optimal solution is achieved. Advantages of genetic algorithms include their ability to handle complex solution spaces, find global optima, adapt to changing conditions, optimize multi-objective functions, and work with non-linear and non-differentiable objective functions. However, they may require significant computational resources and parameter tuning. The article then presents a case study of applying a genetic algorithm to optimize the allocation of a power load in an energy system. The mathematical model is developed, and the simplex method is initially used for solution. Subsequently, a Python program for genetic algorithm implementation is provided. The algorithm's efficiency and convergence are demonstrated through a graphical representation of the optimization process. In conclusion, the article highlights the effectiveness of genetic algorithms in energy optimization, showcasing their rapid convergence and ability to find near-optimal solutions in complex scenarios.

Interpreting Housing Prices with a MultidisciplinaryApproach Based on Nature-Inspired Algorithms and Quantum Computing

Current technology still does not allow the use of quantum computers for broader and individual uses; however, it is possible to simulate some of its potentialities through quantum computing. Quantum computing can be integrated with nature-inspired algorithms to innovatively analyze the dynamics of the real estate market or any other economic phenomenon. With this main aim, this study implements a multidisciplinary approach based on the integration of quantum computing and genetic algorithms to interpret housing prices. Starting from the principles of quantum programming, the work applies genetic algorithms for the marginal price determination of relevant real estate characteristics for a particular segment of Naples’ real estate market. These marginal prices constitute the quantum program inputs to provide, as results, the purchase probabilities corresponding to each real estate characteristic considered. The other main outcomes of this study consist of a comparison of the optimal quantities for each real estate characteristic as determined by the quantum program and the average amounts of the same characteristics but relative to the real estate data sampled, as well as the weights of the same characteristics obtained with the implementation of genetic algorithms. With respect to the current state of the art, this study is among the first regarding the application of quantum computing to interpretation of selling prices in local real estate markets.

A biased random-key genetic algorithm for the chordal completion problem

A graph is chordal if all its cycles of length greater than or equal to four contain a chord, i.e., an edge connecting two nonconsecutive vertices of the cycle. Given a graph G = (V, E), the chordal completion problem consists in finding the minimum set of edges to be added to G to obtain a chordal graph. It has applications in sparse linear systems, database management and computer vision programming. In this article, we developed a biased random-key genetic algorithm (BRKGA) for solving the chordal completion problem, based on the strategy of manipulating permutations that represent perfect elimination orderings of triangulations. Computational results show that the proposed heuristic improve the results of the constructive heuristics fill-in and min-degree. We also developed a strategy for injecting externally constructed feasible solutions coded as random keys into the initial population of the BRKGA that significantly improves the solutions obtained and may benefit other implementations of biased random-key genetic algorithms.

Prediction of enzymatic function with high efficiency and a reduced number of features using genetic algorithm

The post-genomic era has raised a growing demand for efficient procedures to identify protein functions, which can be accomplished by applying machine learning to the characteristics set extracted from the protein. This approach is feature-based and has been the focus of several works in bioinformatics. In this work, we investigated the characteristics of proteins, representing the primary, secondary, tertiary, and quaternary structures of the protein, that improve the model’s quality by applying dimensionality reduction techniques and using the Support Vector Machine classifier for predicting the enzymes’ classes. During the investigation, two approaches were evaluated: feature extraction/transformation, which was performed using the statistical technique Factor Analysis, and feature selection methods. For feature selection, we proposed an approach based on a genetic algorithm to face the optimization conflict between the simplicity and reliability of an ideal representation of the characteristics of the enzymes and also compared and employed other methods for this purpose. The best result was accomplished using a feature subset generated by our implementation of a multi-objective genetic algorithm enriched with features that this work identified as relevant to represent the enzymes. This subset representation reduced the dataset by about 87% and reached 85.78% of F-measure performance, improving the overall quality of the model classification. In addition, we verified in this work a subset addressed with only 28 features out of a total of 424 that reached a performance above 80% of F-measure for four of the six evaluated classes, showing that satisfactory classification performance can be achieved with a reduced number of enzymes’s characteristics. The datasets and implementations are openly available.

The Genetic Algorithm Implementation in Smart Contract for the Blockchain Technology

A blockchain (BC) is merely a decentralized ledger that records each transaction that takes place inside the network. A block is a unit of data that contains encrypted information about every transaction in the network's history. Decentralized technology implementation would necessitate increased security, enforce accountability, and may speed up the transaction from the current hierarchical structure to a decentralized, cooperative chain of command. By producing fresh offspring of those animals who are identifiable by their identification tags, a blockchain is used to conserve the data of a special species of endangered animals. This article presents a decentralized application (SONR DAPPs) by implemented genetic algorithm to forecast a brand-new kind of those animals. The implementation did by solidity framework. Even that the solidity has disadvantages of memory limitation, but the result shows a possibility of using it to implement complex algorithms for the smart contract. This experience gives a chance to develop the solidity for a more complex algorithm to increase the ability o-f choosing better user to be a new block in the chain of this technology.