Differentiate between generic alorithm & tradional alogorith
I want difference points between generic algorithm and traditional algorithm .
please need some points.
artificial-intelligence
asked Nov 19 '18 at 19:03
Nikhil PrabhuNikhil Prabhu
11
add a comment |
I want difference points between generic algorithm and traditional algorithm .
please need some points.
artificial-intelligence
asked Nov 19 '18 at 19:03
Nikhil PrabhuNikhil Prabhu
11
Welcome to StackOverflow. Please read and follow the posting guidelines in the help documentation, as suggested when you created this account. On topic, how to ask, and ... the perfect question apply here. StackOverflow is not a design, coding, research, or tutorial resource.
– Prune
Nov 20 '18 at 18:33
add a comment |
I want difference points between generic algorithm and traditional algorithm .
please need some points.
artificial-intelligence
asked Nov 19 '18 at 19:03
Nikhil PrabhuNikhil Prabhu
11
I want difference points between generic algorithm and traditional algorithm .
please need some points.
artificial-intelligence
artificial-intelligence
asked Nov 19 '18 at 19:03
Nikhil PrabhuNikhil Prabhu
11
asked Nov 19 '18 at 19:03
Nikhil PrabhuNikhil Prabhu
11
asked Nov 19 '18 at 19:03
Nikhil PrabhuNikhil Prabhu
11
asked Nov 19 '18 at 19:03
Nikhil PrabhuNikhil Prabhu
11
asked Nov 19 '18 at 19:03
Nikhil PrabhuNikhil Prabhu
11
11
Welcome to StackOverflow. Please read and follow the posting guidelines in the help documentation, as suggested when you created this account. On topic, how to ask, and ... the perfect question apply here. StackOverflow is not a design, coding, research, or tutorial resource.
– Prune
Nov 20 '18 at 18:33
add a comment |
Welcome to StackOverflow. Please read and follow the posting guidelines in the help documentation, as suggested when you created this account. On topic, how to ask, and ... the perfect question apply here. StackOverflow is not a design, coding, research, or tutorial resource.
– Prune
Nov 20 '18 at 18:33
Welcome to StackOverflow. Please read and follow the posting guidelines in the help documentation, as suggested when you created this account. On topic, how to ask, and ... the perfect question apply here. StackOverflow is not a design, coding, research, or tutorial resource.
– Prune
Nov 20 '18 at 18:33
Welcome to StackOverflow. Please read and follow the posting guidelines in the help documentation, as suggested when you created this account. On topic, how to ask, and ... the perfect question apply here. StackOverflow is not a design, coding, research, or tutorial resource.
– Prune
Nov 20 '18 at 18:33
add a comment |
1 Answer
1
active
oldest
votes
With a little research, i've found a lot of articles. One of the key points is that:
A standard genetic algorithm deals with a set (a population) of
possible solutions (individuals) of a problem. Each individual is a
point in the search space, so we can think of the genetic algorithm as
a multi-point optimization technique for multi-dimensional spaces.
Usually, the size of the population is in the range from 20 to 200 or
300. The majority of traditional optimization methods explores 1, 2, or 3 points in the search space on each iteration.
Traditional methods require a starting point to begin the
optimization. Often the quality of the final solution is very
dependent upon the position of this starting point in the search
space. The choice of a starting point plays a significant role in
finding a good solution to the problem with a large number of local
optima. Genetic algorithms, which offer many solutions and can search
multiple points simultaneously, do not suffer as much from this
drawback.
And also:
Genetic algorithms use probabilistic transition rules, not
deterministic rules
I suggest you to do some research, i've found plenty of articles.
You can start with this article.
answered Nov 19 '18 at 19:16
ManriqueManrique
500114
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53381057%2fdifferentiate-between-generic-alorithm-tradional-alogorith%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
With a little research, i've found a lot of articles. One of the key points is that:
A standard genetic algorithm deals with a set (a population) of
possible solutions (individuals) of a problem. Each individual is a
point in the search space, so we can think of the genetic algorithm as
a multi-point optimization technique for multi-dimensional spaces.
Usually, the size of the population is in the range from 20 to 200 or
300. The majority of traditional optimization methods explores 1, 2, or 3 points in the search space on each iteration.
Traditional methods require a starting point to begin the
optimization. Often the quality of the final solution is very
dependent upon the position of this starting point in the search
space. The choice of a starting point plays a significant role in
finding a good solution to the problem with a large number of local
optima. Genetic algorithms, which offer many solutions and can search
multiple points simultaneously, do not suffer as much from this
drawback.
And also:
Genetic algorithms use probabilistic transition rules, not
deterministic rules
I suggest you to do some research, i've found plenty of articles.
You can start with this article.
answered Nov 19 '18 at 19:16
ManriqueManrique
500114
add a comment |
With a little research, i've found a lot of articles. One of the key points is that:
A standard genetic algorithm deals with a set (a population) of
possible solutions (individuals) of a problem. Each individual is a
point in the search space, so we can think of the genetic algorithm as
a multi-point optimization technique for multi-dimensional spaces.
Usually, the size of the population is in the range from 20 to 200 or
300. The majority of traditional optimization methods explores 1, 2, or 3 points in the search space on each iteration.
Traditional methods require a starting point to begin the
optimization. Often the quality of the final solution is very
dependent upon the position of this starting point in the search
space. The choice of a starting point plays a significant role in
finding a good solution to the problem with a large number of local
optima. Genetic algorithms, which offer many solutions and can search
multiple points simultaneously, do not suffer as much from this
drawback.
And also:
Genetic algorithms use probabilistic transition rules, not
deterministic rules
I suggest you to do some research, i've found plenty of articles.
You can start with this article.
answered Nov 19 '18 at 19:16
ManriqueManrique
500114
add a comment |
With a little research, i've found a lot of articles. One of the key points is that:
A standard genetic algorithm deals with a set (a population) of
possible solutions (individuals) of a problem. Each individual is a
point in the search space, so we can think of the genetic algorithm as
a multi-point optimization technique for multi-dimensional spaces.
Usually, the size of the population is in the range from 20 to 200 or
300. The majority of traditional optimization methods explores 1, 2, or 3 points in the search space on each iteration.
Traditional methods require a starting point to begin the
optimization. Often the quality of the final solution is very
dependent upon the position of this starting point in the search
space. The choice of a starting point plays a significant role in
finding a good solution to the problem with a large number of local
optima. Genetic algorithms, which offer many solutions and can search
multiple points simultaneously, do not suffer as much from this
drawback.
And also:
Genetic algorithms use probabilistic transition rules, not
deterministic rules
I suggest you to do some research, i've found plenty of articles.
You can start with this article.
answered Nov 19 '18 at 19:16
ManriqueManrique
500114
With a little research, i've found a lot of articles. One of the key points is that:
A standard genetic algorithm deals with a set (a population) of
possible solutions (individuals) of a problem. Each individual is a
point in the search space, so we can think of the genetic algorithm as
a multi-point optimization technique for multi-dimensional spaces.
Usually, the size of the population is in the range from 20 to 200 or
300. The majority of traditional optimization methods explores 1, 2, or 3 points in the search space on each iteration.
Traditional methods require a starting point to begin the
optimization. Often the quality of the final solution is very
dependent upon the position of this starting point in the search
space. The choice of a starting point plays a significant role in
finding a good solution to the problem with a large number of local
optima. Genetic algorithms, which offer many solutions and can search
multiple points simultaneously, do not suffer as much from this
drawback.
And also:
Genetic algorithms use probabilistic transition rules, not
deterministic rules
I suggest you to do some research, i've found plenty of articles.
You can start with this article.
answered Nov 19 '18 at 19:16
ManriqueManrique
500114
answered Nov 19 '18 at 19:16
ManriqueManrique
500114
answered Nov 19 '18 at 19:16
ManriqueManrique
500114
answered Nov 19 '18 at 19:16
ManriqueManrique
500114
500114
add a comment |
add a comment |
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53381057%2fdifferentiate-between-generic-alorithm-tradional-alogorith%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Welcome to StackOverflow. Please read and follow the posting guidelines in the help documentation, as suggested when you created this account. On topic, how to ask, and ... the perfect question apply here. StackOverflow is not a design, coding, research, or tutorial resource.
– Prune
Nov 20 '18 at 18:33