Starting at point A, once could go left L , straight S , or right R. Each time you reach the next point crossroads you can choose to go left, straigt, or right. For example, one student in the class can go first left, then right, then left, then straight, and finally straight to reach the final destination of B.
In order for the genetic code to be random, what I did with my students was make them place the three letters L, R, S in an envelope and then choose randomly five times to get the code. Initial conditions in the theory of natural selection are supposed to be random. Only after several generations of mating and mutations does the best species evolve.
Each segment of the pathway has a number associated with it. You can make up an explanation for the number, such as distance, or time it takes to get to the next point. The sum of all the segments will add up to the total number of getting from point A to area B. If we want to get from point A to area B in the shortest amount of time, the pathway with the smallest number is the best.
After all the students in the class have randomly selected their genetic code, have them tally up the time it took them to get from A to B.
This section discusses how the agent decides the next position to which the player moves taking into consideration the positions of the coins, monsters, and fire. Let's start by discussing how the agent uses the positions of the coins to contribute to deciding the player's next position.
As given in the next GIF image, there is more than 1 coin available on the screen. The agent does not consider all coins at once. Instead, the agent sets only 1 coin as a target. After this coin is collected, a new coin is set as the target.
The process continues until collecting all coins. Generally, the agent collects the coins from left to right but this might change based on the positions of the monsters and fire. The next figure shows the order of collecting the coins where the first targeted coin is highlighted with an orange circle and the last coin is highlighted with a red circle.
After collecting the current targeted coin, the agent sets a new coin as a target. The process continues by collecting the current targeted coin and setting a new coin as a target until collecting all the coins. As calculated below, the distances between these 4 solutions and the targeted coin position 0.
Based on these distances, the preferred solution is the first solution 0. As a result, the next position of the player will be 0. Remember that the fitness function of the genetic algorithm should be a maximization function so that a solution with a higher fitness value is preferred.
For this reason, the calculated distances are inverted. The next code shows how the fitness value inverted distance is calculated. The fitness value is saved in the variable output. The fitness values for the 4 solutions are listed below. According to the inverted distances, the best solution of the population is still first solution with value 4.
This means the player's new position is 0. According to the above discussion, it is clear how the coin position is used to decide the player's next position. But the positions of the coins are not the only factor and there still the position of the monsters and the fire. The agent's goal is not only about collecting the coins but also avoiding collisions with the monsters and the fire. The arrow in the next figure marks the targeted coin to be collected by the agent.
If the agent selects the closets solution [from the population] to that coin, then it may collide with the monster which causes a reduction in the player's health and may kill it. Thus, the agent should consider the positions of the monsters while deciding the player's next position. To avoid going to the positions that may cause a reduction to the player's health, the fitness values of the solutions that are close to at least one monster are penalized by reducing their values.
This is how the monsters' positions help to decide the player's next position. If the solution is within a box of width and height of 0. Otherwise, the fitness value is increased by to mark the solution as safe.
The user is free to select other values and see how they affect the agent. The fire are treated similarly to the monsters. If the solution suggested by the genetic algorithm is within a box of width and height of 0. Otherwise, it is increased by After discussing how the agent uses the positions of the coins, monsters, and fire to calculate the fitness value of the solution, here is the code that puts the codes discussed in this section in a single block.
Now, everything is clear about how the agent works. Next is to build the agent using the genetic algorithm. In this project, the genetic algorithm is built using a library named PyGAD. The next section discusses installing PyGAD. PyGAD must be installed to continue with this project.
You can either download its wheel file from PyPI , or use the pip installer, as shown below. Check out the docs for more information on parameters, classes, and submodules in PyGAD. You can also check out my tutorial, 5 Genetic Algorithm Applications Using PyGAD , which covers the library in greater depth and shows how to use it for 5 different use cases. Each problem solved using the genetic algorithm has a fitness function.
GAs can be programmed to search for a range of optimal designs and components for each specific use, or to return results for entirely new types of robots that can perform multiple tasks and have more general application. Evolvable hardware applications are electronic circuits created by GA computer models that use stochastic statistically random operators to evolve new configurations from old ones. As the algorithm does its thing in the running model, eventually a circuit configuration will come along that does what the designer wants.
Think of reconfigurable circuits in something like a space robot. Such GAs would enable self-adaptation and self-repair. Well, GAs are being developed that will allow for dynamic and anticipatory routing of circuits for telecommunications networks.
Using more than one GA circuit-search at a time, soon your interpersonal communications problems may really be all in your head rather than in your telecommunications system. Other GAs are being developed to optimize placement and routing of cell towers for best coverage and ease of switching, so your cell phone and blackberry will be thankful for GAs too. Biomimicry or biomimetics is the development of technologies inspired by designs in nature.
Since GAs are inspired by the mechanisms of biological evolution, it makes sense that they could be used in the process of invention as well. GAs rely primarily on something called implicit parallelism like to like , using mutation and selection in secondary roles toward a design solution. GA programmers are working on applications that not only analyze the natural designs themselves for a return on how they work, but can also combine natural designs to create something entirely new that can have exciting applications.
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