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The water cycle algorithm (WCA), which is a metaheuristic method inspired by the movements of rivers and streams towards the sea in nature, has been adapted and applied here for the first time for solving such a challenging problem as the parameter identification of fermentation process (FP) models. Bacteria and yeast are chosen as representatives of FP models that are subjected to parameter identification due to their impact in different industrial fields. In addition, WCA is considered in comparison with the genetic algorithm (GA), which is another population-based technique that has been proved to be a promising alternative of conventional optimisation methods. The obtained results have been thoroughly analysed in order to outline the advantages and disadvantages of each algorithm when solving such a complicated real-world task. A discussion and a comparative analysis of both metaheuristic algorithms reveal the impact of WCA on model identification problems and show that the newly applied WCA outperforms GA with regard to the model accuracy.

In this paper we present a preliminary version of a new software MkBGFire which is being developed for forest fire spread simulations specific to Bulgarian vegetation. The MkBGFire software is based on the paradigm of the game method for modelling. The game method for modelling is a variant of cellular automata modelling, and similar approaches have gained popularity in the last decade due to the ease of use and their relative simplicity. In particular, they use either square or hexagonal grid. The software presented here is based on a mathematical model and both approaches, vector models and cellular automata models, are implemented. The MkBGFire software uses the OpenGL technology for 2D and 3D visualization of the cells of the game model. Implemented in the preliminary version of the MkBGFire are the following parameters: for the size of the forest cells and the type of the cells – hexagon and square; for control of the speed and the angle of the wind, which is currently constant for the whole terrain; some advanced parameters for control of the fire model. We illustrate the feasibility of the software with some examples using different cell types, forest layouts and wind conditions.

The article considers the possibility of using of the acoustic monitoring of forests and the intercriterial analysis for the early detection of forest fires. It is shown that the sound from the acoustic emission of combustion becomes available for registration earlier than visual observation or registration using smoke or temperature sensors (at least in some cases).

In the context of climatic change, more severe and long-lasting droughts will modify the fitness of plants, with potentially worse consequences on the relict trees. We have investigated the leaf phenotypic (anatomical, physiological and biochemical) plasticity in well-watered, drought-stressed and re-watered plants of two populations of Platanus orientalis, an endangered species in the west of the Mediterranean area. The two populations originated in contrasting climate (drier and warmer, Italy (IT) population, more humid and colder, Bulgaria (BG) population). The IT control plants had thicker leaves, enabling them to maintain higher leaf water content in the dry environment, and more spongy parenchyma, which could improve water conductivity of these plants and may result in easier CO2 diffusion than in BG plants. Control BG plants were also characterized by higher photorespiration and leaf antioxidants compared to IT plants. BG plants responded to drought with greater leaf thickness shrinkage. Drought also caused substantial reduction in photosynthetic parameters of both IT and BG plants. After re-watering, photosynthesis did not fully recover in either of the two populations. However, IT leaves became thicker, while photorespiration in BG plants further increased, perhaps indicating sustained activation of defensive mechanisms. Overall, our hypothesis, that plants with a fragmented habitat (i.e., the IT population) lose phenotypic plasticity but acquire traits allowing better resistance to the climate where they became adapted, remains confirmed.

In this paper, we consider the InterCriteria Analysis (ICrA), which is based on the index matrices and intuitionistic fuzzy sets. We demonstrate the application of ICrA using the software ICrAData. ICrAData implements five different algorithms for InterCriteria relations calculation, namely: μ-biased, Unbiased, ν-biased, Balanced and Weighted. The software ICrAData displays results in two panels – matrix and graphical view, and the results can also be exported in various formats: matrices, vectors, and graphics. In the matrix view, the column data can be sorted in ascending or descending order. The graphic view has options for resizing the intuitionistic fuzzy triangle, showing a grid and assigning different colours to the points. Moreover, a selected point in the graphic is outlined in the matrix view, and vice-versa. In the present paper some of the ICrAData software functionalities are illustrated by an example.

In this paper, the definitions of four operations over 3-dimensional index matrices are discussed. Some of their properties are discussed and an examples for their use are given.

Wireless sensor networks are formed by spatially distributed sensors, which communicate in a wireless way. This network can monitor various kinds of environment and physical conditions like movement, noise, light, humidity, images, chemical substances etc. A given area needs to be fully covered with minimal number of sensors and the energy consumption of the network needs to be minimal too. We propose several algorithms, based on Ant Colony Optimization, to solve the problem. We study the algorithms behavior when the number of ants varies from 1 to 10. We apply InterCriteria analysis to study relations between proposed algorithms and number of ants and analyse correlation between them. Four different algorithms of ICrA—\(\mu \)-biased, Balanced, \(\nu \)-biased and Unbiased—are applied. The obtained results are discussed in order to find the stronger correlations between considered hybrid ACO algorithms.

In combinatorial optimization, the goal is to find the optimal object from a finite set. Since such problems are hard to be solved, usually some metaheuristics is applied. One of the most successful techniques for a number of classes of problems is Ant Colony Optimization (ACO). Some start strategies can be applied, to the ACO algorithms, to improve their performance. Here, the InterCriteria Analysis (ICrA) is applied to the ACO algorithm. On the basis of the ICrA, we examine and analyse the ACO performance according to the different start strategies.

In this investigation one of the promising biological-inspired algorithms, namely artificial bee colony (ABC) algorithm, has been adapted and applied for the first time to parameter identification of S. cerevisiae fed-batch fermentation process model. Several pre-tests adjustments of ABC parameters have been done. As a result, from the parameter identification procedures, the effectiveness and efficacy of ABC algorithm in solving such a complex problem as parameter identification of fermentation process model, have been demonstrated. Also, aiming at better understanding of ABC algorithm performance, two main ABC parameters – number of population and maximum cycle number have been investigated.

The workforce planning is a difficult optimization problem. It is important real life problem which helps organizations to determine workforce which they need. A workforce planning problem is very complex and needs special algorithms to be solved using reasonable computational resources. The problem consists to select set of employers from a set of available workers and to assign this staff to the tasks to be performed. The objective is to minimize the costs associated to the human resources needed to fulfil the work requirements. A good workforce planing is important for an organization to accomplish its objectives. The complexity of this problem does not allow the application of exact methods for instances of realistic size. Therefore we will apply Ant Colony Optimization (ACO) method which is a stochastic method for solving combinatorial optimization problems. On this paper we focus on influence of the parameters on ACO algorithm performance.

Every organization and factory optimize their production process with a help of workforce planing. The aim is minimization of the assignment costs of the workers, who will do the jobs. The problem is very complex and needs exponential number of calculations, therefore special algorithms are developed to be solved. The problem is to select employers and to assign them to the jobs to be performed. This problem has very strong constraints and it is difficult to find feasible solutions. The objective is to fulfil the requirements and to minimize the assignment cost. We propose a hybrid Ant Colony Optimization (ACO) algorithm to solve the workforce problem, which is a combination between ACO and an appropriate local search procedure.

Contemporary InterCriteria analysis (ICrA) approach for searching of existing or unknown correlations between multiple objects against multiple criteria is applied here. Altogether five different algorithms for InterCriteria relations calculation have been examined to render the influence of the genetic algorithm parameters on the algorithm performance. Two cases, i.e. the model parameter identification of E. coli and S. cerevisiae fed-batch fermentation processes, are considered. In this investigation \(\mu \)-biased, Balanced, \(\nu \)-biased, Unbiased, as well as the newly elaborated and proposed here Weighted algorithm have been consequently applied and thoroughly examined. The obtained results for considered here two Case studies have been compared showing that the most reliable algorithm is the \(\mu \)-biased one.

In this chapter, the Artificial bee colony (ABC) algorithm, based on the foraging behaviour of honey bees, is introduced for a numerical optimization problem. The ABC algorithm is one of the efficient population-based biological-inspired algorithms. To demonstrate the usefulness of the presented approach, the ABC algorithm is applied to parameter identification of an E. coli MC4110 fed-batch cultivation process model. The mathematical model of E. coli MC4110 cultivation process is considered as a system of three ordinary differential equations, describing the two main process variables, namely biomass and substrate dynamics, as well as the volume variation. This case study has not been solved previously in the literature by application of ABC algorithm. To obtain a better performance of the ABC algorithm, i.e. high accuracy of the solution within reasonable time, the influence of the algorithm parameters has been investigated. Eight ABC algorithms are applied to parameter identification of the E. coli cultivation process model. The results are compared, based on obtained estimates of model parameters, objective function value, computation time and some statistical measures. As a result, two algorithms are chosen—ABC1 and ABC8, respectively, with 60 × 500 number and 20 × 400 (population×maximum cycle number), such as algorithms with the best performance. Further, the best ABC algorithms are compared with four population-based biological-inspired algorithms, namely Genetic algorithm, Ant colony optimization, Firefly algorithm and Cuckoo search algorithm. The results from literature of metaheuristics applied for the considered here parameter identification problem are used. The results clearly show that the ABC algorithm outperforms the biological-inspired algorithms under consideration, taking into account the overall search ability and computational efficiency.

Of the many postural disorders commonly found in the population, scoliosis is the most complex and the most difficult to diagnose and treat. Adolescent idiopathic scoliosis affects 2% to 4% of adolescent population. Although most of those patients will not develop clinical symptoms, scoliosis can progress to rib deformity and respiratory compromise, and can cause significant cosmetic problems and emotional distress for some patients. Accordingly, early detection is the key factor to prevent the curve magnitude progress, as well as to ensure an appropriate and successful treatment. Proper diagnosis is extremely important for designing a coordinated exercise programs and reliable monitoring progress during treatment. The purpose of the present study is to present a successful example of Generalized Nets application in orthopedics and to propose a novel approach to timely detection of adolescent idiopathic scoliosis and its categorization.

Ant Colony Optimization (ACO) has been used successfully to solve hard combinatorial optimization problems. This metaheuristics method is inspired by the foraging behavior of ant colonies, which manage to establish the shortest routes between their colonies to feeding sources and back. In this paper, ACO algorithms are developed to provide near-optimal solutions for Global Positioning System surveying problem (GSP). In designing Global Positioning System (GPS) surveying network, a given set of earth points must be observed consecutively (schedule). The cost of the schedule is the sum of the time needed to go from one point to another. The problem is to search for the best order in which this observation is executed, minimizing the cost of the schedule. We apply InterCriteria Analysis (ICrA) on the achieved results. Based on ICrA we examine some relations between considered GSPs and ACO algorithm performance.

The workforce planning helps organizations to optimize the production process with aim to minimize the assigning costs. A workforce planning problem is very complex and needs special algorithms to be solved. The problem is to select set of employers from a set of available workers and to assign this staff to the jobs to be performed. Each job requires a time to be completed. For efficiency, a worker must performs a minimum number of hours of any assigned job. There is a maximum number of jobs that can be assigned and a maximum number of workers that can be assigned. There is a set of jobs that shows the jobs on which the worker is qualified. The objective is to minimize the costs associated to the human resources needed to fulfill the work requirements. On this work we propose a variant of Ant Colony Optimization (ACO) algorithm to solve workforce optimization problem. The algorithm is tested on a set of 20 test problems. Achieved solutions are compared with other methods, as scatter search and genetic algorithm. Obtained results show that ACO algorithm performs better than other two algorithms.

In the combinatorial optimization, the goal is to find the optimal object from a finite set of objects. From computational point of view the combinatorial optimization problems are hard to be solved. Therefore on this kind of problems usually is applied some metaheuristics. One of the most successful techniques for a lot of problem classes is metaheuristic algorithm Ant Colony Optimization (ACO). Some start strategies can be applied on ACO algorithms to improve the algorithm performance. We propose several start strategies when an ant chose first node, from which to start to create a solution. Some of the strategies are base on forbidding some of the possible starting nodes, for one or more iterations, because we suppose that no good solution starting from these nodes. The aim of other strategies are to increase the probability to start from nodes with expectations that there are good solutions starting from these nodes. We can apply any of the proposed strategy separately or to combine them. In this investigation InterCriteria Analysis (ICrA) is applied on ACO algorithms with the suggested different start strategies. On the basis of ICrA the ACO performance is examined and analysed.

In this paper two metaheuristics: Ant Colony Optimization (ACO) and Genetic Algorithms (GA), were compared for parameter identification of an E. coli fed-batch cultivation process model. A system of ordinary differential equations was used to model the biomass growth and substrate utilization. Parameter optimization was performed using a real experimental data set from an E. coli MC4110 fed-batch cultivation process. The GA and ACO adjustments were done based on several pre-tests on the optimization problem considered here. Two techniques were compared based on the obtained “best”, “worst” and “average” values for estimates and the objective function J. The results showed that the “best” value of the objective function J is achieved by ACO. At the same time, GA achieved better results for “worst” and “average” values. Analyzing the results, it could be concluded that both algorithms: ACO and GA, perform satisfactorily for the problem of parameter optimization of an E. coli fed-batch cultivation pr...

In this paper, a hybrid scheme using Bat Algorithm (BA) and Sequential Quadratic Programming (SQP) method is introduced. In the hybrid BA—SQP, the role of BA is to generate feasible solutions to a problem. The role of SQP is to exploit the information gathered by BA. This process obtains a solution which is at least as good as—but usually better than—the best solution devised by BA. To demonstrate the usefulness of the presented approach, the hybrid scheme was applied to parameter identification of an E. coli MC4110 fed-batch cultivation process model. A comparison with both the conventional BA and SQP method is presented. The results showed that the hybrid BA-SQP has the advantages of both BA's global search ability and SQP's local search ability, thus enhancing the overall search ability and computational efficiency. For comparison, the results obtained by applying Ant colony optimization algorithm in conditions similar to those of BA are further shown.

International audience; In this paper we apply InterCriteria Analysis (ICrA), which is based on the apparatus of Index Matrices and Intuitionistic Fuzzy Sets. We apply ICrA on the well-known Ant Colony Optimization (ACO) general framework including environment change. The environment is simulated by means of the Logistic map, that is used in ACO for perturbing the update of the pheromone trails. We compare different levels of perturbation of the one of the most important parameters in ACO – the pheromone. Based on ICrA we examine the obtained identification results and discuss the conclusions about existing relations and dependencies between defined criteria, defined in terms of ICrA.

Osteoporosis is a growing major public health problem with impact that crosses medical, social, and economic lines. It is now recognized that it is extremely important to diagnose osteoporosis before a fragility fracture occurs. The purpose of the present study is to give an example of application of Generalized Nets in orthopedics and traumatology and to propose a novel approach for diagnosing the asymptomatic osteoporosis in elderly patients.

In this investigation different algorithms for InterCriteria relations calculation are proposed. The algorithms are investigated by exploring the influence of genetic parameters on algorithm performance during the model parameter identification of E. coli fermentation process. Four different algorithms performing InterCriteria Analysis (ICrA), namely μ-biased, balanced, ν-biased and unbiased, are applied. Proposed ICrA algorithms are compared based on real experimental data set of an E. coli MC4110 fed-batch fermentation process. The obtained results show that for considered here case study the most reliable algorithm is the μ-biased one.

This paper presents an optimal tuning of a universal digital PID controller using metaheuristics as Genetic Algorithms (GA), Simulated Annealing (SA) and Tabu Search (TS). The controllers were used...

A method for finding the optimal feeding profile for whey fermentation by strain Kluyveromyces lactic MC 5 in a fed-batch bioreactor was developed. The optimal profile maximizes the process effectiveness and minimizes the bioprocess duration. The method is based on Neuro-dynamic Programming (NDP), wherein the optimal control decision is parameterized in the form of a cost-to-go function. The suggested method employs simulations from a heuristic feeding strategy as an initial point to generate the cost-to-go to experimental data. A neural network is applied to obtain cost-to-go as a function of system state. Iterations of the Bellman equation are included to improve the cost function. Thus, the obtained approach guarantees optimal control of the bioreactor when disturbances are present. The developed approach was compared with other methods—the Pontryagin's Maximum Principle and Fuzzy Sets Theory. The NDP method provided better results than the other methods.

This paper focuses on design of a glucose concentration control system based on nonlinear model plant of E. coli MC4110 fed-batch cultivation process. Due to significant time delay in real time glucose concentration measurement, a correction is proposed in glucose concentration measurement and a Smith predictor (SP) control structure based on universal PID controller is designed. To reduce the influence of model error in SP structure the estimate of measured glucose concentration is used. For the aim an extended Kalman filter (EKF) is designed. To achieve good closed-loop system performance genetic algorithm (GA) based optimal controller tuning procedure is applied. A standard binary encoding GA is applied. The GA parameters and operators are specified for the considered here problem. As a result the optimal PID controller settings are obtained. The simulation experiments of the control systems based on SP with EKF and without EKF are performed. The results show that the control system based on SP with EKF has a better performance than the one without EKF. For a short time the controller sets the control variable and maintains it at the desired set point during the cultivation process. As a result, a high biomass concentration of 48.3 g·l-1 is obtained at the end of the process.

In this paper we apply InterCriteria Analysis (ICrA) approach based on the apparatus of Index Matrices and Intuitionistic Fuzzy Sets. The main idea is to use ICrA to establish the existing relations and dependencies of defined parameters in a non-linear model of an E. coli fed-batch cultivation process. We perform a series of model identification procedures applying Genetic Algorithms (GAs). We proposed a schema of ICrA of ICrA results to examine the obtained model identification results. The discussion about existing relations and dependencies is performed according to criteria defined in terms of ICrA. We consider as ICrA criteria model parameters and GAs outcomes on the one hand, and 14 differently tuned GAs on the other. Based on the results, we observe the mutual relations between model parameters and GAs outcomes, such as computation time and objective function value. Moreover, some conclusions about the preferred tuned GAs for the considered model parameter identification in terms of achieved accuracy for given computation time are presented.

In this investigation the InterCriteria Analysis (ICrA) approach is applied. The apparatuses of index matrices and intuitionistic fuzzy sets are at the core of ICrA. They are used to examine the influences of two main genetic algorithms (GA) parameters—the rates of crossover (xovr) and mutation (mutr). A series of parameter identification procedures for S. cerevisiae and E. coli fermentation process models is fulfilled. Twenty GA with different xovr and mutr values are applied. Relations between ICrA criteria—GA parameters and outcomes, on the one hand, and fermentation process model parameters, on the other hand, are investigated. The ICrA approach is applied by pairs, as well as by triples. The obtained results are thoroughly analysed towards computation time and model accuracy and some conclusions about the derived criteria interactions are reported.

This paper presents the implementation of functional state approach to modelling of Escherichia coli fed-batch cultivation. Due to the complex metabolic pathways of microorganisms, the accurate modelling of bioprocesses is rather difficult. The functional state approach of a process is an alternative concept which helps in modelling and control of complex processes. The approach main idea is developing of models based on multiple submodels for each functional states (operating regime). In each functional state the process is described by a conventional type of model, called the local model, which is valid in this state. For parameter identification of the model the genetic algorithms are used. Genetic algorithms are directed random search techniques, applying the mechanics of natural selection and natural genetics, which can find the global optimal solution in complex multidimensional search spaces. Based on the available experimental data and simulations of E. coli fed-batch cultivation it is sho...

In this paper, a hybrid scheme, to solve optimization problems, using a Genetic Algorithm (GA) and an Ant Colony Optimization (ACO) is introduced. In the hybrid GA-ACO approach, the GA is used to find a feasible solutions to the considered optimization problem. Next, the ACO exploits the information gathered by the GA. This process obtains a solution, which is at least as good as—but usually better than—the best solution devised by the GA. To demonstrate the usefulness of the presented approach, the hybrid scheme is applied to the parameter identification problem in the E. coli MC4110 fed-batch fermentation process model. Moreover, a comparison with both the conventional GA and the stand-alone ACO is presented. The results show that the hybrid GA-ACO takes the advantages of both the GA and the ACO, thus enhancing the overall search ability and computational efficiency of the solution method.

In this paper, the functional state modelling approach is validated for modelling of the cultivation of two different microorganisms: yeast (Saccharomyces cerevisiae) and bacteria (Escherichia coli). Based on the available experimental data for these fed-batch cultivation processes, three different functional states are distinguished, namely primary product synthesis state, mixed oxidative state and secondary product synthesis state. Parameter identification procedures for different local models are performed using genetic algorithms. The simulation results show high degree of adequacy of the models describing these functional states for both S. cerevisiae and E. coli cultivations. Thus, the local models are validated for the cultivation of both microorganisms. This fact is a strong structure model verification of the functional state modelling theory not only for a set of yeast cultivations, but also for bacteria cultivation. As such, the obtained results demonstrate the efficiency and efficacy of the functional state modelling approach.

This chapter presents a comparison of population-based (e.g. Genetic Algorithms (GA), Firefly Algorithm (FA), and Ant Colony Optimization (ACO))and single point search meta-heuristic methods (e.g. Simulated Annealing (SA), Threshold Accepting (TA), and Tabu Search (TS)) applied to an optimal tuning of a universal digital proportional-integral-derivative (PID) controller. The PID controllers control feed rate and maintain glucose concentration at the desired set point for an E. coli MC4110 fed-batch cultivation process. The model of the cultivation process is represented by dynamic non-linear mass balance equations for biomass and substrate. In the control the design measurement, process noise, and time delay of the glucose measurement system were taken into account. To achieve good closed-loop system the constants (Kp, Ti, Td, b, c and N) were tuned in the PID controller algorithm so the controller can provide control action designed for the specific process requirements resulting in an optimal set of PID controller settings. For a time the controllers set and maintained the control variable at the desired set point during the E. coli MC4110 fed-batch cultivation process. Average, best, and worst objective function values and PID controller's parameters were used as criteria to compare the performance of the considered meta-heuristic algorithms. This indicates that the population-based meta-heuristics performs better than the single point search methods. GA and ACO show better performance than FA. It also indicates that TS results are comparable to those of FA. The results show that SA and TA algorithms failed to solve the PID controller tuning problem.

The apparatus of Generalized Nets (GN) is applied here to describe different kinds of genetic algorithms (GA). Failure of conventional optimization methods to lead to a satisfied solution in parameter identification of non-linear and time-dependent parameters provokes an idea some stochastic algorithms to be applied. As such genetic algorithms (GA), as a promising metaheuristic technique, are widely used. Different modifications of simple genetic algorithms (SGA) have been investigated and successfully applied to parameter identification of fermentation processes aiming to improve the model accuracy and the algorithm convergence time. Altogether six modifications of SGA have been proposed with a different sequence of implementation of basic genetic operators selection, crossover and mutation. In the present GN model the user is allowed to choose the sequence of execution of main GA operators, thus resulting in one of the six considered here modifications of SGA. © 2012 IEEE.

Classical biotechnology is the science of production of human-useful processes and products under controlled conditions, applying biological agents – microorganisms, plant or animal cells, their exoand endoproducts, e.g. enzymes, etc. (Viesturs et al., 2004). The conventional agriculture or chemistry cannot perform these processes as efficiently or at all. In fact, conventional biotechnology has been the largest industrial activity on earth for a very long time. Modern biotechnology goes much further with respect to control of the biological processes.

The apparatus of Generalized Nets is applied here to a description of a selection operator, which is one of the basic genetic algorithm operators. This genetic operator performs a probabilistic selection based upon the individual's fittness such that the better individuals have an increased chance of being selected for the next generation. The resulting GN model could be considered as a separate module, but can also be accumulated into a GN model to describe a whole genetic algorithm.

The paper presents the implementation of multiple model approach to modelling of Escherichia coli BL21(DE3)pPhyt109 fed-batch cultivation processes for an extracellular production of bacterial phytase. Due to the complex metabolic pathways of microorganisms, the accurate modelling of bioprocesses is rather difficult. Multiple model approach is an alternative concept which helps in modelling and control of complex processes. The main idea is the development of a model based on simple submodels for the purposes of further high quality process control. The presented simulations of E. coli fed-batch cultivation show how the process could be divided into different functional states and how the model parameters could be obtained easily using genetic algorithms. The obtained results and model verification demonstrate the effectiveness of the applied concept of multiple model approach and of the proposed identification scheme. © 2007 by Pontificia Universidad Católica de Valparaíso.

The present work offers a novel approach to parameter identification of an E. coli cultivation process model, using hybrids of three metaheuristics - ant colony optimisation ACO, firefly algorithm FA and genetic algorithm GA. The motivation behind this hybridisation is to combine the benefits of these approaches, aimed at achieving commensurate calculations precision with less computation resources, in terms of time and memory. The proposed hybrids are approbated with the estimation of the parameters of a real E. coli cultivation process model. In the considered non-linear mathematical model three parameters are estimated, namely, maximum specific growth rate, saturation constant and yield coefficient. Based on the numerical and simulation results, it is shown that the models obtained by the proposed hybrid algorithms are competitive with standard ACO, FA and GA. The hybrids show two advantages - have much less running time and require much less memory compared to standard ACO, FA and GA.

A modified genetic algorithm is proposed for a parameter identification of an E. coli fed-batch fermentation model. In the simple genetic algorithm the best chromosome does not always keep on improving in each generation. Obtained good solutions could be destroyed by either crossover or mutation or both operations. The aim of the modified genetic algorithm is to prevent this disadvantage. In view of the fact that there is no general theory about tuning the genetic algorithm parameters, some adjustments of genetic parameters, according to the regarded problem, are done to improve the conventional genetic algorithm. The simulation results illustrate that the use of proposed modified genetic algorithm for a parameter identification of fermentation processes is highly efficient and effective. The functions and parameter adjustments, proposed here, enhance the algorithm performance. Still more, the implementation of the modified genetic algorithm leads to noticeable decreasing of the solution time.