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is compiled twice. Once while the program is written and second when the program is executed at the user's site. The first compilation is done by your C# builder and the second by the .NET Framework on the user's machine. The reason why C# compiled applications could be faster is that, during the second compilation, the compiler knows the actual run-time environment and processor type and could generate instructions that targets a specific processor.

A well designed C# program is more than 90% as fast as an equivalent "well-designed" C++ program

The problem with "not-freeing" the memory at the right time is that the working set of the application increases which increases the number of "page faults"

In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. Therefore it is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.

In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. Therefore it is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.

A Voyage to Functional Programming in F#

Finally I settled on F#. Both LISP and F# are belong to functional programming (FP) paradigm.Functional programming is a complete different paradigm from imperative (and OO) programming.I found it terse as well yet I can understand it (though some of the concepts are new to me).The advantage is it can use .NET Framework. Thus it gives me a break from OOP and yet taps the power from the familiar .NET Framework.Therefore I decided to dive into it to seem whether functional programming can deliver its claims in terms of productivity and expression power.I would like to share my findings in this blog to those interested in learning functional programming.

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An Abstract Agent Programming Language or Artificial Autonomous Agents Programming Language or 3APL (pronounced triple-A-P-L) is an experimental tool and programming language for the development, implementation and testing of multiple cognitive agents using the Belief-Desire-Intention (BDI) approach. The newest incarnation of 3APL is 2APL (A Practical Agent Programming Language).

So bone up on JavaScript, CSS, and HTML to succeed over the next five years

Every top-flight developer I’ve met recommends learning at least one dynamic or functional programming language to learn new ways of thinking, and from personal experience, I can tell you that it works.

Dynamic programming problems are to be solved with tabular methods

breadth-first search

Memorize the value of pi You should always try to remember the value of pi as far as possible, 3.1415926535897932384626433832795, certainly the part in italics. The judges may not give the value in the question, and if you use values like 22/7 or 3.1416 or 3.142857, then it is very likely that some of the critical judge inputs will cause you to get the wrong answer. You can also get the value of pi as a compiler-defined constant or from the following code: Pi=2*acos(0)

Flocking behavior is the behavior exhibited when a group of birds, called a flock, are foraging or in flight. There are parallels with the shoaling behavior of fish, or the swarming behavior of insects. Computer simulations and mathematical models which have been developed to emulate the flocking behaviors of birds can generally be applied also to the "flocking" behavior of other species. As a result, the term "flocking" is sometimes applied, in computer science, to species other than birds. This article is about the modelling of flocking behavior. From the perceptive of the mathematical modeller, "flocking" is the collective motion of a large number of self-propelled entities and is a collective animal behavior exhibited by many living beings such as birds, fish, bacteria, and insects.[1] It is considered an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination. Flocking behavior was first simulated on a computer in 1986 by Craig Reynolds with his simulation program, Boids. This program simulates simple agents (boids) that are allowed to move according to a set of basic rules. The result is akin to a flock of birds, a school of fish, or a swarm of insects.

Flocking behavior is the behavior exhibited when a group of birds, called a flock, are foraging or in flight. There are parallels with the shoaling behavior of fish, or the swarming behavior of insects. Computer simulations and mathematical models which have been developed to emulate the flocking behaviors of birds can generally be applied also to the "flocking" behavior of other species. As a result, the term "flocking" is sometimes applied, in computer science, to species other than birds. This article is about the modelling of flocking behavior. From the perceptive of the mathematical modeller, "flocking" is the collective motion of a large number of self-propelled entities and is a collective animal behavior exhibited by many living beings such as birds, fish, bacteria, and insects.[1] It is considered an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination. Flocking behavior was first simulated on a computer in 1986 by Craig Reynolds with his simulation program, Boids. This program simulates simple agents (boids) that are allowed to move according to a set of basic rules. The result is akin to a flock of birds, a school of fish, or a swarm of insects.

Rich Internet applications (RIAs) are web applications that have some of the characteristics of desktop applications, typically delivered by way of a proprietary web browser plug-ins or independently via sandboxes or virtual machines[1]. Examples of RIA frameworks include Curl (programming language), Adobe Flash/Adobe Flex/AIR, Java/JavaFX[2], uniPaaS[3] and Microsoft Silverlight[4].

What is Domain Name Resolution Introduction When using the Internet most people connect to web sites, ftp servers or other Internet servers by connecting to a domain name, as in www.bleepingcomputer.com. Internet applications, though, do not communicate via domain names, but rather using IP addresses, such as 192.168.1.1. Therefore when you type a domain name in your program that you wish to connect to, your application must first convert it to an IP address that it will use to connect to. The way these hostnames are resolved to their mapped IP address is called Domain Name Resolution. On almost all operating systems whether they be Apple, Linux, Unix, Netware, or Windows the majority of resolutions from domain names to IP addresses are done through a procedure called DNS.

Hosts File - There is a file called the HOSTS file that you can use to convert domain names to IP addresses. Entries in the HOSTS file override any mappings that would be resolved via a DNS server

Netbios - This only applies to Windows machines and will only be used to map names to IP addresses if all previous methods failed. This method will attempt to map the netbios name you are trying to connect to with an IP address.

ELIZA was a computer program and an early example (by modern standards) of primitive natural language processing. ELIZA operated by processing users' responses to scripts, the most famous of which was DOCTOR, a simulation of a Rogerian psychotherapist. In this mode, ELIZA mostly rephrased the user's statements as questions and posed those to the 'patient.' ELIZA was written by Joseph Weizenbaum between 1964 to 1966