In order to understand the fitness function, you first have to understand that a genetic algorithm is one which changes over time (it evolves). In nature we have things like predators and harsh environments which eliminate unwanted specimens of animals (a slow zebra will get eaten by a lion). We need to simulate this behavior when programming genetic algorithms.
The fitness function basically determines which possible solutions get passed on to multiply and mutate into the next generation of solutions. This is usually done by analyzing the "genes," which hold some data about a particular solution to the problem you are trying to solve. The fitness function will look at the genes and make some qualitative assessment, returning a fitness value for that solution. The rest of the genetic algorithm will discard any solutions with a "poor" fitness value and accept any with a "good" fitness value.
In short: the goal of a fitness function is to provide a meaningful, measurable, and comparable value given a set of genes.
Do you mean, "the difference between an algorithm that runs in polynomial time, and one that runs in exponential time".First a real quick review. A polynomial is any equation of the formy = cmxm + ... + c2x2 + c1x + c0 ,where ci are constantsAn exponential function is something of the formy = cxThese functions grow much faster than any polynomial function.So, if T(n) describes the runtime of an algorithm as a function of whatever (# of inputs, size of input, etc.)., and T(n) can be bound above by any polynomic function, then we say that algorithm runs in polynomial time.If it can't be bound above by a polynomial function, but can be bound above by an exponential function, we say it runs in exponential time.Note how ugly an exponential algorithm is. By adding one more input, we roughly double (or triple, whatever c is) the run-time.
A recursive call in an algorithm is when a function (that implements this algorithm) calls itself. For example, Quicksort is a popular algorithm that is recursive. The recursive call is seen in the last line of the pseudocode, where the quicksort function calls itself. function quicksort('array') create empty lists 'less' and 'greater' if length('array') ≤ 1 return 'array' // an array of zero or one elements is already sorted select and remove a pivot value 'pivot' from 'array' for each 'x' in 'array' if 'x' ≤ 'pivot' then append 'x' to 'less' else append 'x' to 'greater' return concatenate(quicksort('less'), 'pivot', quicksort('greater'))
Performance measurement is concerned with obtaining the space and time requirement of a particular algorithm thus quantities depend on the and absence used as well as on computer on which the algorithm is run..........
A sequential algorithm has the following characteristics:a dependence on the standard environment,a relevant name,a main method (function/subroutine) with no parameters,supplementary methods using a top-down modular design,input of boolean values,output exemplifying the relevant criteria.
Algorithm is deterministic if for a given input the output generated is same for a function. A mathematical function is deterministic. Hence the state is known at every step of the algorithm.Algorithm is non deterministic if there are more than one path the algorithm can take. Due to this, one cannot determine the next state of the machine running the algorithm. Example would be a random function.FYI,Non deterministic machines that can't solve problems in polynomial time are NP. Hence finding a solution to an NP problem is hard but verifying it can be done in polynomial time. Hope this helps.Pl correct me if I am wrong here.Thank you.Sharada
Genetic Algorithm
Here is an example MATLAB code for designing an FIR filter with a rectangular window using a genetic algorithm: % Define the desired filter specifications Fs = 1000; % Sampling frequency Fc = 100; % Cutoff frequency N = 51; % Filter order % Define the fitness function for the genetic algorithm fitnessFunc = @(x) designFIR(x, Fs, Fc); % Define the genetic algorithm options options = optimoptions('ga', 'Display', 'iter', 'MaxGenerations', 100); % Run the genetic algorithm to find the optimal filter coefficients [x, fval] = ga(fitnessFunc, N, options); % Design the FIR filter using the obtained coefficients filter = fir1(N-1, x); % Plot the frequency response of the designed filter freqz(filter, 1, 1024, Fs); In the above code, designFIR is a user-defined function that evaluates the fitness of an FIR filter design based on its frequency response. The genetic algorithm is then used to optimize the filter coefficients to meet the desired specifications. Finally, the designed filter is plotted using the freqz function.
A function is an algorithm that returns a value when it is executed
direct mapping doesn't need replacement algorithm
alu alogrithm logic unit
yes it can. k means algorithm for grouping particles and its used for multimodal function optimization
MD stands for Message Digest algorithm.
physical fitness is ability to function effeciently and effectively
physical fitness
Usually it's modelled by the function (alpha)e^(-alpha *x).
Time complexity is a function which value depend on the input and algorithm of a program and give us idea about how long it would take to execute the program
Do you mean, "the difference between an algorithm that runs in polynomial time, and one that runs in exponential time".First a real quick review. A polynomial is any equation of the formy = cmxm + ... + c2x2 + c1x + c0 ,where ci are constantsAn exponential function is something of the formy = cxThese functions grow much faster than any polynomial function.So, if T(n) describes the runtime of an algorithm as a function of whatever (# of inputs, size of input, etc.)., and T(n) can be bound above by any polynomic function, then we say that algorithm runs in polynomial time.If it can't be bound above by a polynomial function, but can be bound above by an exponential function, we say it runs in exponential time.Note how ugly an exponential algorithm is. By adding one more input, we roughly double (or triple, whatever c is) the run-time.